NexusData
680029924
21/01/2022
# Formatting chunk
# Clear workspace
rm(list = ls())
# Specify libraries
library(tidyverse)
library(lubridate)
library(naniar)
# Read data from web
cameroon_read <- read_csv(url("https://datashare.ed.ac.uk/bitstream/handle/10283/3804/Heat-Covid%20Nexus%20Survey_Cameroon.csv?sequence=1&isAllowed=y"), n_max = 2000)
indonesia_read <- read_csv(url("https://datashare.ed.ac.uk/bitstream/handle/10283/3804/Heat-Covid%20Nexus%20Survey_Indonesia.csv?sequence=2&isAllowed=y"), n_max = 2000)
pakistan_read <- read_csv(url("https://datashare.ed.ac.uk/bitstream/handle/10283/3804/Heat-Covid%20Nexus%20Survey_Pakistan.csv?sequence=14&isAllowed=y"), n_max = 2000)
india_read <- read_csv(url("https://datashare.ed.ac.uk/bitstream/handle/10283/3804/Heat-Covid%20Nexus%20Survey_India.csv?sequence=15&isAllowed=y"), n_max = 2000)
# Crop objects
pakistan_cropped <- pakistan_read %>% filter(Country == "Pakistan")
india_cropped <- india_read %>% filter(Country == "India")
# Generate R-legible names
names(cameroon_read) <- str_replace_all(names(cameroon_read), c(" " = "_",
"/" = "_or_",
"'" = ""))
names(indonesia_read) <- str_replace_all(names(indonesia_read), c(" " = "_",
"/" = "_or_",
"'" = ""))
names(pakistan_cropped) <- str_replace_all(names(pakistan_cropped), c(" " = "_",
"/" = "_or_",
"'" = ""))
names(india_cropped) <- str_replace_all(names(india_cropped), c(" " = "_",
"/" = "_or_",
"'" = ""))
# Tidy objects
cameroon_factors <- c("Country", "Language", "Agree", "Littoral", "Gender", "Occupation", "Occupation_Translated", "Occupation_Coded",
"OwnHouse", "CoolHome", "Electricity", "DrinkingWater", "HouseholdPurposes", "RoofingMaterial", "WallMaterial",
"FloorMaterial", "Windows", "NormallyDo", "NormallyDo_Translated", "NormallyDo_Coded", "Add_ventilation",
"Cant_go_outside_(COVID_related)", "Clean_floor_with_water", "Do_nothing_specific", "Draw_curtains",
"Drink_cold_drink", "Drink_warm_drink", "Drink_water", "Go_outside", "Sit_under_tree", "Sleep_outside",
"Sleep_or_lie_on_floor", "Sleep_or_rest", "Smoke", "Stay_at_home", "Take_a_bath_or_shower", "Turn_off_lights",
"Turn_on_power_source", "Unclear", "Undress_or_change_clothes_or_wet_clothes", "Use_a_cooler", "Use_AC", "Use_fan",
"Use_manual_fan_or_cooling_device", "Use_talcum_powder", "PandemicChange", "YesPandemicChange",
"YesPandemicChange_Translated", "YesPandemicChange_Recoded", "SpendAtHome", "AmountOfTime", "Income",
"EatingHabits", "WaterIntake", "ElectricityUsage", "HealthServices", "HomeWarm", "TemperatureInsideHome",
"TemperatureOutsideHome", "Experience_Blurred_vision", "Experience_Clammy_skin", "Experience_Concentration_loss",
"Experience_Confusion", "Experience_Convulsions", "Experience_Dizziness", "Experience_DONT_KNOW",
"Experience_Fainting_[brief_loss_of_consciousness]", "Experience_Fatigue", "Experience_Feeling_hot",
"Experience_Feeling_listless_or_lack_of_engagement_with_social_activities_or_family", "Experience_Feeling_sweaty",
"Experience_Feeling_thirsty", "Experience_Headache", "Experience_Irrational_behaviour",
"Experience_Loss_of_consciousness_[extensive]", "Experience_Muscle_cramps", "Experience_Muscle_weakness",
"Experience_Nausea", "Experience_Poor_quality_of_sleep", "Experience_Rash", "Experience_REFUSED",
"Experience_Vomiting", "ExperienceBeforeHeat", "ExperienceBeforeHeat_(Blurred_vision)",
"ExperienceBeforeHeat_(Clammy_skin)", "ExperienceBeforeHeat_(Concentration_loss)", "ExperienceBeforeHeat_(Confusion)",
"ExperienceBeforeHeat_(Convulsions)", "ExperienceBeforeHeat_(Dizziness)",
"ExperienceBeforeHeat_(Fainting_[brief_loss_of_consciousness])", "ExperienceBeforeHeat_(Fatigue)",
"ExperienceBeforeHeat_(Feeling_hot)",
"ExperienceBeforeHeat_(Feeling_listless_or_lack_of_engagement_with_social_activities_or_family)",
"ExperienceBeforeHeat_(Feeling_sweaty)", "ExperienceBeforeHeat_(Feeling_thirsty)", "ExperienceBeforeHeat_(Headache)",
"ExperienceBeforeHeat_(Irrational_behaviour)", "ExperienceBeforeHeat_(Loss_of_consciousness_[extensive])",
"ExperienceBeforeHeat_(Muscle_cramps)", "ExperienceBeforeHeat_(Muscle_weakness)", "ExperienceBeforeHeat_(Nausea)",
"ExperienceBeforeHeat_(Poor_quality_of_sleep)", "ExperienceBeforeHeat_(Rash)", "ExperienceBeforeHeat_(Vomiting)",
"FeelingMore", "PhysicalConflict", "HeatAffect")
cameroon_tidy <- cameroon_read %>% mutate_at(cameroon_factors, factor) %>% rename(Region = Littoral)
indonesia_factors <- c("Country", "Agree", "ADM-1", "Gender", "Occupation", "Occupation_Translated", "Occupation_Coded", "OwnHouse",
"CoolHome", "Electricity", "DrinkingWater", "HouseholdPurposes", "RoofingMaterial", "WallMaterial", "FloorMaterial",
"Windows", "NormallyDo", "NormallyDo_Translated", "NormallyDo_Coded", "Add_ventilation",
"Cant_go_outside_(COVID_related)", "Clean_floor_with_water", "Do_nothing_specific", "Draw_curtains",
"Drink_cold_drink", "Drink_warm_drink", "Drink_water", "Go_outside", "Sit_under_tree", "Sleep_outside",
"Sleep_or_lie_on_floor", "Sleep_or_rest", "Smoke", "Stay_at_home", "Take_a_bath_or_shower", "Turn_off_lights",
"Turn_on_power_source", "Unclear", "Undress_or_change_clothes_or_wet_clothes", "Use_a_cooler", "Use_AC", "Use_fan",
"Use_manual_fan_or_cooling_device", "Use_talcum_powder", "PandemicChange", "YesPandemicChange",
"YesPandemicChange_Translated", "YesPandemicChange_CodedGeneral", "YesPandemicChange_CodedSpecific",
"SpendAtHome", "AmountOfTime", "Income", "EatingHabits", "WaterIntake", "ElectricityUsage", "HealthServices",
"HomeWarm", "TemperatureInsideHome", "TemperatureOutsideHome", "Experience_Blurred_vision", "Experience_Clammy_skin",
"Experience_Concentration_loss", "Experience_Confusion", "Experience_Convulsions", "Experience_Dizziness",
"Experience_DONT_KNOW", "Experience_Fainting_[brief_loss_of_consciousness]", "Experience_Fatigue",
"Experience_Feeling_hot", "Experience_Feeling_listless_or_lack_of_engagement_with_social_activities_or_family",
"Experience_Feeling_sweaty", "Experience_Feeling_thirsty", "Experience_Headache", "Experience_Irrational_behaviour",
"Experience_Loss_of_consciousness_[extensive]", "Experience_Muscle_cramps", "Experience_Muscle_weakness",
"Experience_Nausea", "Experience_Poor_quality_of_sleep", "Experience_Rash", "Experience_REFUSED",
"Experience_Vomiting", "ExperienceBeforeHeat", "ExperienceBeforeHeat_(Blurred_vision)",
"ExperienceBeforeHeat_(Clammy_skin)", "ExperienceBeforeHeat_(Concentration_loss)", "ExperienceBeforeHeat_(Confusion)",
"ExperienceBeforeHeat_(Convulsions)", "ExperienceBeforeHeat_(Dizziness)",
"ExperienceBeforeHeat_(Fainting_[brief_loss_of_consciousness])", "ExperienceBeforeHeat_(Fatigue)",
"ExperienceBeforeHeat_(Feeling_hot)",
"ExperienceBeforeHeat_(Feeling_listless_or_lack_of_engagement_with_social_activities_or_family)",
"ExperienceBeforeHeat_(Feeling_sweaty)", "ExperienceBeforeHeat_(Feeling_thirsty)",
"ExperienceBeforeHeat_(Headache)", "ExperienceBeforeHeat_(Irrational_behaviour)",
"ExperienceBeforeHeat_(Loss_of_consciousness_[extensive])", "ExperienceBeforeHeat_(Muscle_cramps)",
"ExperienceBeforeHeat_(Muscle_weakness)", "ExperienceBeforeHeat_(Nausea)",
"ExperienceBeforeHeat_(Poor_quality_of_sleep)", "ExperienceBeforeHeat_(Rash)",
"ExperienceBeforeHeat_(Vomiting)", "FeelingMore", "PhysicalConflict", "HeatAffect")
indonesia_tidy <- indonesia_read %>% mutate_at(indonesia_factors, factor) %>%
select(1:114) %>%
rename("YesPandemicChange_Recoded" = "YesPandemicChange_CodedSpecific") %>%
select(-YesPandemicChange_CodedGeneral) %>%
mutate("Language" = NA, .before = "Agree") %>%
rename("Region" = "ADM-1")
pakistan_factors <- c("Country", "Agree", "Region", "Gender", "Occupation", "Occupation_Translated", "Occupation_Coded", "OwnHouse",
"CoolHome", "Electricity", "DrinkingWater", "HouseholdPurposes", "RoofingMaterial", "WallMaterial", "FloorMaterial",
"Windows", "NormallyDo", "NormallyDo_Translated", "NormallyDo_Coded", "Add_ventilation",
"Cant_go_outside_(COVID_related)", "Clean_floor_with_water", "Do_nothing_specific", "Draw_curtains",
"Drink_cold_drink", "Drink_warm_drink", "Drink_water", "Go_outside", "Sit_under_tree", "Sleep_outside",
"Sleep_or_lie_on_floor", "Sleep_or_rest", "Smoke", "Stay_at_home", "Take_a_bath_or_shower", "Turn_off_lights",
"Turn_on_power_source", "Unclear", "Undress_or_change_clothes_or_wet_clothes", "Use_a_cooler", "Use_AC", "Use_fan",
"Use_manual_fan_or_cooling_device", "Use_talcum_powder", "PandemicChange", "YesPandemicChange",
"YesPandemicChange_Translated", "YesPandemicChange_Recoded",
"SpendAtHome", "AmountOfTime", "Income", "EatingHabits", "WaterIntake", "ElectricityUsage", "HealthServices",
"HomeWarm", "TemperatureInsideHome", "TemperatureOutsideHome", "Experience_Blurred_vision", "Experience_Clammy_skin",
"Experience_Concentration_loss", "Experience_Confusion", "Experience_Convulsions", "Experience_Dizziness",
"Experience_DONT_KNOW", "Experience_Fainting_[brief_loss_of_consciousness]", "Experience_Fatigue",
"Experience_Feeling_hot", "Experience_Feeling_listless_or_lack_of_engagement_with_social_activities_or_family",
"Experience_Feeling_sweaty", "Experience_Feeling_thirsty", "Experience_Headache", "Experience_Irrational_behaviour",
"Experience_Loss_of_consciousness_[extensive]", "Experience_Muscle_cramps", "Experience_Muscle_weakness",
"Experience_Nausea", "Experience_Poor_quality_of_sleep", "Experience_Rash", "Experience_REFUSED",
"Experience_Vomiting", "ExperienceBeforeHeat", "ExperienceBeforeHeat_(Blurred_vision)",
"ExperienceBeforeHeat_(Clammy_skin)", "ExperienceBeforeHeat_(Concentration_loss)", "ExperienceBeforeHeat_(Confusion)",
"ExperienceBeforeHeat_(Convulsions)", "ExperienceBeforeHeat_(Dizziness)",
"ExperienceBeforeHeat_(Fainting_[brief_loss_of_consciousness])", "ExperienceBeforeHeat_(Fatigue)",
"ExperienceBeforeHeat_(Feeling_hot)",
"ExperienceBeforeHeat_(Feeling_listless_or_lack_of_engagement_with_social_activities_or_family)",
"ExperienceBeforeHeat_(Feeling_sweaty)", "ExperienceBeforeHeat_(Feeling_thirsty)",
"ExperienceBeforeHeat_(Headache)", "ExperienceBeforeHeat_(Irrational_behaviour)",
"ExperienceBeforeHeat_(Loss_of_consciousness_[extensive])", "ExperienceBeforeHeat_(Muscle_cramps)",
"ExperienceBeforeHeat_(Muscle_weakness)", "ExperienceBeforeHeat_(Nausea)",
"ExperienceBeforeHeat_(Poor_quality_of_sleep)", "ExperienceBeforeHeat_(Rash)",
"ExperienceBeforeHeat_(Vomiting)", "FeelingMore", "PhysicalConflict", "HeatAffect")
pakistan_tidy <- pakistan_cropped %>%
rename("Region" = "ADM-1") %>%
mutate(Region = Sindh) %>%
select(-Sindh, -YesPandemicChange_CodedGeneral, -Language2) %>%
mutate("Occupation_Translated" = NA, .before = "Occupation_Coded") %>%
rename("YesPandemicChange_Recoded" = "YesPandemicChange_CodedSpecific") %>%
rename("ExperienceBeforeHeat_(Concentration_loss)" = "ExperineceBeforeHeat_(Concentration_loss)") %>%
mutate_at(pakistan_factors, factor)
india_factors <- c("Country", "Agree", "Region", "Gender", "Occupation", "Occupation_Translated", "Occupation_Coded", "OwnHouse",
"CoolHome", "Electricity", "DrinkingWater", "HouseholdPurposes", "RoofingMaterial", "WallMaterial", "FloorMaterial",
"Windows", "NormallyDo", "NormallyDo_Translated", "NormallyDo_Coded", "Add_ventilation",
"Cant_go_outside_(COVID_related)", "Clean_floor_with_water", "Do_nothing_specific", "Draw_curtains",
"Drink_cold_drink", "Drink_warm_drink", "Drink_water", "Go_outside", "Sit_under_tree", "Sleep_outside",
"Sleep_or_lie_on_floor", "Sleep_or_rest", "Smoke", "Stay_at_home", "Take_a_bath_or_shower", "Turn_off_lights",
"Turn_on_power_source", "Unclear", "Undress_or_change_clothes_or_wet_clothes", "Use_a_cooler", "Use_AC", "Use_fan",
"Use_manual_fan_or_cooling_device", "Use_talcum_powder", "PandemicChange", "YesPandemicChange",
"YesPandemicChange_Translated", "YesPandemicChange_Recoded",
"SpendAtHome", "AmountOfTime", "Income", "EatingHabits", "WaterIntake", "ElectricityUsage", "HealthServices",
"HomeWarm", "TemperatureInsideHome", "TemperatureOutsideHome", "Experience_Blurred_vision", "Experience_Clammy_skin",
"Experience_Concentration_loss", "Experience_Confusion", "Experience_Convulsions", "Experience_Dizziness",
"Experience_DONT_KNOW", "Experience_Fainting_[brief_loss_of_consciousness]", "Experience_Fatigue",
"Experience_Feeling_hot", "Experience_Feeling_listless_or_lack_of_engagement_with_social_activities_or_family",
"Experience_Feeling_sweaty", "Experience_Feeling_thirsty", "Experience_Headache", "Experience_Irrational_behaviour",
"Experience_Loss_of_consciousness_[extensive]", "Experience_Muscle_cramps", "Experience_Muscle_weakness",
"Experience_Nausea", "Experience_Poor_quality_of_sleep", "Experience_Rash", "Experience_REFUSED",
"Experience_Vomiting", "ExperienceBeforeHeat", "ExperienceBeforeHeat_(Blurred_vision)",
"ExperienceBeforeHeat_(Clammy_skin)", "ExperienceBeforeHeat_(Concentration_loss)", "ExperienceBeforeHeat_(Confusion)",
"ExperienceBeforeHeat_(Convulsions)", "ExperienceBeforeHeat_(Dizziness)",
"ExperienceBeforeHeat_(Fainting_[brief_loss_of_consciousness])", "ExperienceBeforeHeat_(Fatigue)",
"ExperienceBeforeHeat_(Feeling_hot)",
"ExperienceBeforeHeat_(Feeling_listless_or_lack_of_engagement_with_social_activities_or_family)",
"ExperienceBeforeHeat_(Feeling_sweaty)", "ExperienceBeforeHeat_(Feeling_thirsty)",
"ExperienceBeforeHeat_(Headache)", "ExperienceBeforeHeat_(Irrational_behaviour)",
"ExperienceBeforeHeat_(Loss_of_consciousness_[extensive])", "ExperienceBeforeHeat_(Muscle_cramps)",
"ExperienceBeforeHeat_(Muscle_weakness)", "ExperienceBeforeHeat_(Nausea)",
"ExperienceBeforeHeat_(Poor_quality_of_sleep)", "ExperienceBeforeHeat_(Rash)",
"ExperienceBeforeHeat_(Vomiting)", "FeelingMore", "PhysicalConflict", "HeatAffect")
india_tidy <- india_cropped %>%
select(-`ADM-1-IND1`, -`ADM-1-IND2`, -`ADM-1-IND3`, -`ADM-1-IND4`) %>%
rename("Region" = "ADM-1-IND5") %>%
mutate("Occupation_Translated" = NA, .before = "Occupation_Coded") %>%
rename("YesPandemicChange_Recoded" = "YesPandemicChange_CodedSpecific") %>%
select(-YesPandemicChange_CodedGeneral, -Language2) %>%
rename("Temperature" = "Temperature_(C)", "Heat_Index" = "Heat_Index_(C)") %>%
mutate_at(india_factors, factor)
# Join data to make a new dataset including ALL four countries - drop one row from Indonesia (lacks Country variable)
# Subsets to only those households with maximum 24 hours per day of electricity
# Also tidy some variables and create new variables
nexusdata <- bind_rows(cameroon_tidy, indonesia_tidy, pakistan_tidy, india_tidy) %>%
filter(!is.na(Country)) %>%
mutate(Language = as_factor(Language)) %>%
mutate(OptIn_Date = dmy(OptIn_Date)) %>%
mutate(Region = recode(Region, DOUALA = "Douala", 'Jakarta Special Capital Region' = "Jakarta")) %>%
mutate(City = as_factor(str_c(Country, " - ", Region))) %>%
mutate(Country = fct_relevel(Country, c("Pakistan",
"India",
"Indonesia",
"Cameroon"))) %>%
mutate(Gender = as_factor(str_to_sentence(Gender))) %>%
mutate(DrinkingWater = as_factor(recode(DrinkingWater,
"BOTTLES" = "bottled water",
'COMMUNAL TAP/SHARED TAP [ALSO CALLED STAND PIPE/STREET END TAP/YARD TAP]' = "shared tap",
'SUPPLIED IN TANKS' = "supplied in tanks",
"OTHER" = "other"))) %>%
mutate(HouseholdPurposes = recode(HouseholdPurposes,
'COMMUNAL OR STREET-END TAP' = "shared tap",
"OTHER" = "other",
'SUPPLIED IN TANKS' = "supplied in tanks")) %>%
filter(HoursDay<25) %>% # Remove unrealistic hours of electricity per day
filter(People_in_House<16) %>% # Remove outliers (99.9% of the data)
mutate(SpendAtHome = as_factor(str_to_sentence(SpendAtHome))) %>%
mutate(SpendAtHome = fct_relevel(SpendAtHome, c("Most of the day - except for short trips out",
"Half of the day",
"Some of the day",
"Very little - only for sleeping",
"Other",
"Don't know",
"Refused"))) %>%
mutate(AmountOfTime = as_factor(str_to_sentence(AmountOfTime))) %>%
mutate(AmountOfTime = fct_relevel(AmountOfTime, c("Significantly increased",
"Slightly increased",
"Remained about the same",
"Slightly decreased",
"Significantly decreased",
"Don't know",
"Refused"))) %>%
mutate(Income = as_factor(str_to_sentence(Income))) %>%
mutate(Income = fct_relevel(Income, c("Significantly increased",
"Slightly increased",
"Remained about the same",
"Slightly decreased",
"Significantly decreased",
"Don't know",
"Refused"))) %>%
mutate(Occupation_Coded = as_factor(str_to_sentence(Occupation_Coded))) %>%
mutate(EatingHabits = fct_relevel(EatingHabits, c("I EAT MORE - IT IS ENOUGH",
"NO CHANGE - IT IS ENOUGH",
"NO CHANGE - IT IS NOT ENOUGH",
"I EAT LESS - IT IS ENOUGH",
"I EAT LESS - IT IS NOT ENOUGH",
"DON'T KNOW"))) %>%
mutate(WaterIntake = as_factor(str_to_sentence(WaterIntake))) %>%
mutate(WaterIntake = as_factor(recode(WaterIntake,
"Yes - i use more" = "Yes - I use more",
"Yes - i use less" = "Yes - I use less"))) %>%
mutate(WaterIntake = fct_relevel(WaterIntake, c("Yes - I use more",
"No - no change",
"Yes - I use less",
"Don't know",
"Refused"))) %>%
mutate(ElectricityUsage = fct_relevel(ElectricityUsage, c("I HAVE BEEN ABLE TO USE MORE ENERGY",
"NO CHANGE",
"I HAVE HAD TO USE LESS ENERGY",
"DON'T KNOW",
"REFUSED"))) %>%
mutate(ElectricityUsage = as_factor(str_to_sentence(ElectricityUsage))) %>%
mutate(Electricity = str_to_sentence(Electricity)) %>%
mutate(Electricity = fct_relevel(Electricity, c("Solar",
"Generator",
"12-volt battery",
"Informal connection",
"Other"))) %>%
mutate(HealthServices = as_factor(str_to_sentence(HealthServices))) %>%
mutate(HealthServices = fct_relevel(HealthServices, c("Harder",
"The same",
"Easier",
"Refused",
"Don't know"))) %>%
mutate(HomeWarm = as_factor(str_to_sentence(HomeWarm))) %>%
mutate(HomeWarm = fct_relevel(HomeWarm, c("Very hot",
"Hot",
"Warm",
"Neutral",
"Slightly cool",
"Cool",
"Cold",
"Don't know"))) %>%
mutate(TemperatureInsideHome = as_factor(str_to_sentence(TemperatureInsideHome))) %>%
mutate(TemperatureInsideHome = fct_relevel(TemperatureInsideHome, c("Very hot",
"Hot",
"Warm",
"Slightly warm",
"Comfortable",
"Slightly cool",
"Cool",
"Cold",
"Don't know",
"Refused"))) %>%
mutate(TemperatureOutsideHome = as_factor(str_to_sentence(TemperatureOutsideHome))) %>%
mutate(TemperatureOutsideHome = fct_relevel(TemperatureOutsideHome, c("Intolerable",
"Very difficult to tolerate",
"Fairly difficult to tolerate",
"Slightly difficult to tolerate",
"Perfectly tolerable",
"Don't know",
"Refused"))) %>%
mutate(Experience_DONT_KNOW = fct_explicit_na(Experience_DONT_KNOW, na_level = "No")) %>%
select(!c(Agree, Occupation, Occupation_Translated, YesPandemicChange, YesPandemicChange_Translated, NormallyDo, NormallyDo_Translated))
# Make some new variables
newnexus <- nexusdata %>%
mutate(Employment = as_factor(recode(Occupation_Coded, # Note that this has already been done into four categories - see report
'EMPLOYED IN PRIVATE COMPANY' = "Working",
'GOVERNMENT WORK' = "Working",
'PAID LABOR/WAGE' = "Working",
'HOUSEWIFE' = "Homemaker",
'HOUSEWIFE/HUSBAND/HOMEMAKER' = "Homemaker",
'AGRICULTURE/HUSBANDRY/FISHING' = "Working",
'INFORMAL TRADE / STREET SALES' = "Working",
'BUSINESS/SELF-EMPLOYED' = "Working",
'STUDENT' = "Student",
'UNEMPLOYED' = "Not working",
'GOVERNMENT ASSISTANCE / SOCIAL PROTECTION NETWORKS [PENSIONS]' = "Not working",
'MIGRANT REMITTANCES [FROM OUTSIDE OF THE COUNTRY]' = "Not working",
'GOVERNMENT ASSISTANCE/SOCIAL PROTECTION NETWORKS [PENSIONS]' = "Not working",
'HELP FROM FAMILY AND/OR FRIENDS [FROM INSIDE OF THE COUNTRY]' = "Not working"))) %>%
mutate(wallmass = recode(WallMaterial,
"BAMBOO POLES OR WOOD" = "Lightweight",
"MUD BRICK [CRUDE/RAW/COOKED]" = "Heavyweight",
"PALM FRONDS/WOVEN MATS" = "Lightweight",
"REINFORCED CONCRETE" = "Heavyweight",
"DON'T KNOW"= "Don't know",
"REFUSED" = "Refused")) %>%
mutate(roofmass = recode(RoofingMaterial,
"BAMBOO" = "Lightweight",
"GALVANISED IRON"= "Lightweight",
"PALM FRONDS"= "Lightweight",
"REINFORCED CONCRETE"= "Heavyweight",
"SHEET METAL [TIN/ZINC/CORRUGATED IRON]"= "Lightweight",
"T-GIRDER"= "Heavyweight",
"WOODEN SHINGLES"= "Lightweight",
"CERAMIC TILE"= "Heavyweight",
"DON'T KNOW"= "Don't know",
"DRIED CLAY"= "Heavyweight",
"REFUSED" = "Refused")) %>%
mutate(walltherm = recode(WallMaterial, "BAMBOO POLES OR WOOD" = "Lightweight low thermal conductivity",
"MUD BRICK [CRUDE/RAW/COOKED]" = "Heavyweight low thermal conductivity",
"PALM FRONDS/WOVEN MATS" = "Lightweight low thermal conductivity",
"REINFORCED CONCRETE" = "Heavyweight high thermal conductivity",
"DON'T KNOW"= "Don't know",
"REFUSED" = "Refused")) %>%
mutate(rooftherm = recode(RoofingMaterial,
"BAMBOO" = "Lightweight low thermal conductivity",
"GALVANISED IRON"= "Lightweight high thermal conductivity",
"PALM FRONDS"= "Lightweight low thermal conductivity",
"REINFORCED CONCRETE"= "Heavyweight high thermal conductivity",
"SHEET METAL [TIN/ZINC/CORRUGATED IRON]"= "Lightweight high thermal conductivity",
"T-GIRDER"= "Heavyweight high thermal conductivity",
"WOODEN SHINGLES"= "Lightweight low thermal conductivity",
"CERAMIC TILE"= "Heavyweight low thermal conductivity",
"DON'T KNOW"= "Don't know",
"DRIED CLAY"= "Heavyweight low thermal conductivity",
"REFUSED" = "Refused")) %>%
mutate(TemperatureInsideHomeCat = recode(TemperatureInsideHome,
"very hot" = "Hot",
"hot" = "Hot",
"warm" = "Hot",
"slightly warm" = "Hot",
"comfortable" = "Comfortable",
"slightly cool" = "Cold",
"cool" = "Cold",
"cold" = "Cold")) %>%
mutate(HoursDayCat = cut(HoursDay, breaks=seq(0,24,4), labels=c("0-4","5-8","9-12", "13-16", "17-20", "21-24"))) %>%
mutate(pseudoID = row_number()) %>%
mutate(WallMaterial = str_to_sentence(WallMaterial)) %>%
mutate(RoofingMaterial = str_to_sentence(RoofingMaterial))
nexusNormallyDo <- newnexus %>% pivot_longer(cols = Add_ventilation:Use_talcum_powder,
names_to = "NormallyDo_New",
values_to = "NormallyDo_Selection") %>%
group_by(NormallyDo_New, NormallyDo_Selection) %>%
mutate(NormallyDo_New = as_factor(NormallyDo_New))
nexusExperience <- newnexus %>% pivot_longer(cols = Experience_Blurred_vision:Experience_Vomiting,
names_to = "Experience_New",
values_to = "Experience_Selection") %>%
group_by(Experience_New, Experience_Selection) %>%
mutate(Experience_New = as_factor(Experience_New))
nexusExperienceBeforeHeat <- newnexus %>% pivot_longer(cols = `ExperienceBeforeHeat_(Blurred_vision)`:`ExperienceBeforeHeat_(Vomiting)`,
names_to = "ExperienceBeforeHeat_New",
values_to = "ExperienceBeforeHeat_Selection") %>%
group_by(ExperienceBeforeHeat_New, ExperienceBeforeHeat_Selection) %>%
mutate(ExperienceBeforeHeat_New = as_factor(ExperienceBeforeHeat_New))Graphs from previous paper
Pakistan
library(tidyverse)
# Graphs for report
cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") # From http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette
newnames <- names(newnexus)
# Page 33
ggplot(data = newnexus %>% filter(Country=="Pakistan") %>%
pivot_longer(cols = Add_ventilation:Use_talcum_powder,
names_to = "NormallyDo_New",
values_to = "Selection") %>%
group_by(NormallyDo_New, Selection) %>%
mutate(NormallyDo_New = as_factor(NormallyDo_New)) %>%
summarise(n = n()) %>%
filter(Selection == "Yes") %>%
arrange(desc(n))) +
geom_bar(aes(x = reorder(NormallyDo_New, n), y = n), stat = "identity", fill = "#999999") +
scale_x_discrete(labels = c("Take_a_bath_or_shower" = "Take a bath or shower",
"Go_outside" = "Go outside",
"Use_fan" = "Use fan",
"Do_nothing_specific" = "Do nothing specific",
"Drink_cold_drink" = "Drink cold drink",
"Use_manual_fan_or_cooling_device" = "Use manual fan or cooling device",
"Add_ventilation" = "Add ventilation",
"Turn_on_power_source" = "Turn on power source",
"Drink_water" = "Drink water",
"Use_a_cooler" = "Use a cooler",
"Clean_floor_with_water" = "Clean floor with water",
"Stay_at_home" = "Stay at home",
"Sleep_or_rest" = "Sleep or rest",
"Use_talcum_powder" = "Use talcum powder",
"Use_AC" = "Use AC",
"Undress_or_change_clothes_or_wet_clothes" = "Undress or change clothes or wet clothes",
"Drink_warm_drink" = "Drink warm drink",
"Sit_under_tree" = "Sit under tree")) +
xlab("Strategies normally used to manage heat") +
coord_flip()
# Page 34
newnexus %>%
filter(Country == "Pakistan") %>%
group_by(SpendAtHome, Gender) %>%
count() %>%
group_by(Gender) %>%
mutate(percentage = n/sum(n)) %>%
ggplot(aes(x = SpendAtHome, y = percentage, fill = Gender)) +
geom_bar(position = "dodge", stat = "identity") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual(values = cbPalette) +
xlab("Time spent at home before pandemic") +
ylab("Percentage of group")
# Page 35
newnexus %>%
filter(Country == "Pakistan") %>%
group_by(AmountOfTime, Gender) %>%
count() %>%
group_by(Gender) %>%
mutate(percentage = n/sum(n)) %>%
ggplot(aes(x = AmountOfTime, y = percentage, fill = Gender)) +
geom_bar(position = "dodge", stat = "identity") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual(values = cbPalette) +
xlab("Change in time at home after pandemic") +
ylab("Percentage of group")
# Page 36
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
geom_bar(aes(x = Electricity), position = "dodge", fill = "#999999") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
ylab("n") +
xlab("Source of electricity")
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
geom_bar(aes(x = HoursDay), fill = "#999999") +
scale_x_continuous(breaks=seq(0,24,1)) +
xlab("Hours of electricity per day") +
ylab("n")
# Page 37
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
geom_bar(aes(x = Electricity, fill = HoursDayCat), position = "dodge") +
scale_fill_manual(values = cbPalette) +
xlab("Source of electricity") +
ylab("n") +
labs(fill = "Hours of electricity per day")
# Page 38
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
geom_bar(aes(x = reorder(ElectricityUsage, desc(ElectricityUsage))), fill = "#999999") +
coord_flip() +
xlab("Change in use of electricity") +
ylab("n") +
theme(legend.position = "none") 
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
geom_bar(aes(x = reorder(ElectricityUsage, desc(ElectricityUsage)), fill = City), position = "dodge") +
coord_flip() +
scale_fill_manual(values = cbPalette) +
xlab("Change in use of electricity") +
ylab("n") +
facet_grid(rows = vars(City)) +
theme(legend.position = "none")
# Page 39
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
geom_bar(aes(x = reorder(WaterIntake, desc(WaterIntake))), fill = "#999999") +
coord_flip() +
xlab("Change in water intake") +
ylab("n")
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
geom_bar(aes(x = reorder(WaterIntake, desc(WaterIntake)), fill = City), position = "dodge") +
coord_flip() +
scale_fill_manual(values = cbPalette) +
xlab("Change in water intake") +
ylab("n") +
facet_grid(rows = vars(City)) 
theme(legend.position = "none")## List of 1
## $ legend.position: chr "none"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUEggplot(data = newnexus %>% filter(Country=="Pakistan")) +
geom_bar(aes(x = NormallyDo_Coded)) +
coord_flip() # clearly some problems with NormallyDo_Coded - too many categories - need to be reorganised according to cats in report page 33
ggplot(data = newnexus %>% filter(Country=="Pakistan")) +
geom_bar(aes(x = SpendAtHome, fill = Gender), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) # Works okay but needs the categories to be reordered
ggplot(data = newnexus %>% filter(Country=="Pakistan")) +
geom_bar(aes(x = AmountOfTime, fill = Gender), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) # Works okay but we should use less stereotyping colours for the genders
ggplot(data = newnexus %>% filter(Country=="Pakistan", HoursDay < 25)) +
geom_histogram(aes(x = HoursDay), position = "dodge") + #Still got some 88 value in here that should go
scale_x_continuous(n.breaks = 24) +
stat_ecdf(aes(x = HoursDay), geom = "step", pad = "FALSE")
geom_line(aes(y = aes(cumsum(HoursDay), x = seq(1:24)))) # This doesn't work## mapping: y = ~aes(cumsum(HoursDay), x = seq(1:24))
## geom_line: na.rm = FALSE, orientation = NA
## stat_identity: na.rm = FALSE
## position_identityIndia
# Page 51
ggplot(data = newnexus %>% filter(Country == "India")) +
geom_bar(aes(x = Electricity, fill = HoursDayCat), position = "dodge") +
scale_fill_manual(values = cbPalette) +
xlab("Source of electricity") +
ylab("n") +
labs(fill = "Hours of electricity per day")
ggplot(data = newnexus %>% filter(Country=="India") %>%
pivot_longer(cols = Add_ventilation:Use_talcum_powder,
names_to = "NormallyDo_New",
values_to = "Selection") %>%
group_by(NormallyDo_New, Selection) %>%
mutate(NormallyDo_New = as_factor(NormallyDo_New)) %>%
summarise(n = n()) %>%
filter(Selection == "Yes") %>%
arrange(desc(n))) +
geom_bar(aes(x = reorder(NormallyDo_New, n), y = n), stat = "identity", fill = "#E69F00") +
scale_x_discrete(labels = c("Take_a_bath_or_shower" = "Take a bath or shower",
"Go_outside" = "Go outside",
"Use_fan" = "Use fan",
"Do_nothing_specific" = "Do nothing specific",
"Drink_cold_drink" = "Drink cold drink",
"Use_manual_fan_or_cooling_device" = "Use manual fan or cooling device",
"Add_ventilation" = "Add ventilation",
"Turn_on_power_source" = "Turn on power source",
"Drink_water" = "Drink water",
"Use_a_cooler" = "Use a cooler",
"Clean_floor_with_water" = "Clean floor with water",
"Stay_at_home" = "Stay at home",
"Sleep_or_rest" = "Sleep or rest",
"Use_talcum_powder" = "Use talcum powder",
"Use_AC" = "Use AC",
"Undress_or_change_clothes_or_wet_clothes" = "Undress or change clothes or wet clothes",
"Drink_warm_drink" = "Drink warm drink",
"Sit_under_tree" = "Sit under tree",
"Cant_go_outside_(COVID_related)" = "Can't go outside (COVID-related)")) +
xlab("Strategies normally used to manage heat") +
coord_flip()
# Page 52
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
filter(Country == "India") %>%
group_by(SpendAtHome, Gender) %>%
count() %>%
group_by(Gender) %>%
mutate(percentage = n/sum(n)) %>%
ggplot(aes(x = SpendAtHome, y = percentage, fill = Gender)) +
geom_bar(position = "dodge", stat = "identity") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual(values = cbPalette) +
xlab("Change in time at home after pandemic") +
ylab("Percentage of group")
# Page 53
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
filter(Country == "India") %>%
group_by(AmountOfTime, Gender) %>%
count() %>%
group_by(Gender) %>%
mutate(percentage = n/sum(n)) %>%
ggplot(aes(x = AmountOfTime, y = percentage, fill = Gender)) +
geom_bar(position = "dodge", stat = "identity") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual(values = cbPalette) +
xlab("Change in time at home after pandemic") +
ylab("Percentage of group")
Indonesia
# Page 66
ggplot(data = newnexus %>% filter(Country == "Indonesia")) +
geom_bar(aes(x = Occupation_Coded), fill = "#56B4E9") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
xlab("Occupation") +
ylab("n")
ggplot(data = newnexus %>% filter(Country == "Indonesia")) +
geom_bar(aes(x = Electricity, fill = HoursDayCat), position = "dodge") +
ylab("n") +
xlab("Source of electricity") +
labs(fill = "Hours of electricity per day")
# Page 67
ggplot(data = newnexus %>% filter(Country=="Indonesia") %>%
pivot_longer(cols = Add_ventilation:Use_talcum_powder,
names_to = "NormallyDo_New",
values_to = "Selection") %>%
group_by(NormallyDo_New, Selection) %>%
mutate(NormallyDo_New = as_factor(NormallyDo_New)) %>%
summarise(n = n()) %>%
filter(Selection == "Yes") %>%
arrange(desc(n))) +
geom_bar(aes(x = reorder(NormallyDo_New, n), y = n), stat = "identity", fill = "#56B4E9") +
scale_x_discrete(labels = c("Use_fan" = "Use fan",
"Add_ventilation" = "Add ventilation",
"Go_outside" = "Go outside",
"Take_a_bath_or_shower" = "Take a bath or shower",
"Undress_or_change_clothes_or_wet_clothes" = "Undress or change clothes or wet clothes",
"Use_AC" = "Use AC",
"Use_manual_fan_or_cooling_device" = "Use manual fan or cooling device",
"Drink_water" = "Drink water",
"Sleep_or_lie_on_floor" = "Sleep or lie on floor",
"Sit_under_tree" = "Sit under tree",
"Turn_off_lights" = "Turn off lights",
"Sleep_or_rest" = "Sleep or rest")) +
xlab("Strategies normally used to manage heat") +
coord_flip()
# Page 68
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
filter(Country == "Indonesia") %>%
group_by(SpendAtHome, Gender) %>%
count() %>%
group_by(Gender) %>%
mutate(percentage = n/sum(n)) %>%
ggplot(aes(x = SpendAtHome, y = percentage, fill = Gender)) +
geom_bar(position = "dodge", stat = "identity") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual(values = cbPalette) +
xlab("Time spent at home before pandemic") +
ylab("Percentage of group")
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
filter(Country == "Indonesia") %>%
group_by(AmountOfTime, Gender) %>%
count() %>%
group_by(Gender) %>%
mutate(percentage = n/sum(n)) %>%
ggplot(aes(x = AmountOfTime, y = percentage, fill = Gender)) +
geom_bar(position = "dodge", stat = "identity") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual(values = cbPalette) +
xlab("Change in time spent at home after pandemic") +
ylab("Percentage of group")
# Page 74
ggplot(data = newnexus %>% filter(Country=="Indonesia") %>%
group_by(YesPandemicChange_Recoded) %>%
summarise(n = n()) %>%
filter(YesPandemicChange_Recoded != "N/A") %>%
arrange(-n)) +
geom_bar(aes(x = reorder(YesPandemicChange_Recoded, n), y = n), stat = "identity", fill = "#56B4E9") +
xlab("Strategies to manage heat during pandemic") +
coord_flip()
Cameroon
# Page 82
ggplot(data = newnexus %>% filter(Country == "Cameroon")) +
geom_bar(aes(x = Occupation_Coded), fill = "#009E73") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
xlab("Occupation") +
ylab("n")
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
filter(Country == "Cameroon") %>%
group_by(Occupation_Coded, Gender) %>%
count() %>%
group_by(Gender) %>%
mutate(percentage = n/sum(n)) %>%
ggplot(aes(x = Occupation_Coded, y = percentage, fill = Gender)) +
geom_bar(position = "dodge", stat = "identity") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual(values = cbPalette) +
xlab("Occupation") +
ylab("Percentage of group")
# Page 84
ggplot(data = newnexus %>% filter(Country=="Cameroon") %>%
pivot_longer(cols = Add_ventilation:Use_talcum_powder,
names_to = "NormallyDo_New",
values_to = "Selection") %>%
group_by(NormallyDo_New, Selection) %>%
mutate(NormallyDo_New = as_factor(NormallyDo_New)) %>%
summarise(n = n()) %>%
filter(Selection == "Yes") %>%
arrange(desc(n))) +
geom_bar(aes(x = reorder(NormallyDo_New, n), y = n), stat = "identity", fill = "#009E73") +
scale_x_discrete(labels = c("Take_a_bath_or_shower" = "Take a bath or shower",
"Go_outside" = "Go outside",
"Use_fan" = "Use fan",
"Do_nothing_specific" = "Do nothing specific",
"Drink_cold_drink" = "Drink cold drink",
"Use_manual_fan_or_cooling_device" = "Use manual fan or cooling device",
"Add_ventilation" = "Add ventilation",
"Turn_on_power_source" = "Turn on power source",
"Drink_water" = "Drink water",
"Use_a_cooler" = "Use a cooler",
"Clean_floor_with_water" = "Clean floor with water",
"Stay_at_home" = "Stay at home",
"Sleep_or_rest" = "Sleep or rest",
"Use_talcum_powder" = "Use talcum powder",
"Use_AC" = "Use AC",
"Undress_or_change_clothes_or_wet_clothes" = "Undress or change clothes or wet clothes",
"Drink_warm_drink" = "Drink warm drink",
"Sit_under_tree" = "Sit under tree",
"Cant_go_outside_(COVID_related)" = "Can't go outside (COVID-related)",
"Draw_curtains" = "Draw curtains",
"Sleep_or_lie_on_floor" = "Sleep or lie on floor")) +
xlab("Strategies normally used to manage heat") +
coord_flip()
# Page 85
ggplot(data = newnexus %>% filter(Country == "Cameroon")) +
geom_bar(aes(x = Electricity, fill = HoursDayCat), position = "dodge") +
ylab("n") +
xlab("Source of electricity") +
labs(fill = "Hours of electricity per day")
# Page 86
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
filter(Country == "Cameroon") %>%
group_by(SpendAtHome, Gender) %>%
count() %>%
group_by(Gender) %>%
mutate(percentage = n/sum(n)) %>%
ggplot(aes(x = SpendAtHome, y = percentage, fill = Gender)) +
geom_bar(position = "dodge", stat = "identity") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual(values = cbPalette) +
xlab("Time spent at home before pandemic") +
ylab("Percentage of group")
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
filter(Country == "Cameroon") %>%
group_by(AmountOfTime, Gender) %>%
count() %>%
group_by(Gender) %>%
mutate(percentage = n/sum(n)) %>%
ggplot(aes(x = AmountOfTime, y = percentage, fill = Gender)) +
geom_bar(position = "dodge", stat = "identity") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_y_continuous(labels = scales::percent) +
scale_fill_manual(values = cbPalette) +
xlab("Change in time spent at home after pandemic") +
ylab("Percentage of group")
All countries
# Page 97
# No age distribution data in my data sets
ggplot(data = newnexus) +
geom_bar(aes(x = Country, fill = Gender), position = "dodge") +
scale_fill_manual(values = cbPalette) +
ylab("n")
# Page 98
ggplot(data = newnexus) +
geom_bar(aes(x = Occupation_Coded, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
xlab("Occupation") +
ylab("n")
# Page 99
ggplot(data = newnexus) +
geom_bar(aes(x = RoofingMaterial, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1)
# Page 100
ggplot(data = newnexus) +
geom_bar(aes(x = WallMaterial, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1)
ggplot(data = newnexus) +
geom_bar(aes(x = FloorMaterial, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1)
# Page 103
ggplot(data = newnexus) +
geom_bar(aes(x = TemperatureOutsideHome, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1) +
xlab("Thermal comfort outside the home")
ggplot(data = newnexus) +
geom_bar(aes(x = TemperatureInsideHome, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1) +
xlab("Thermal comfort inside the home")
# Page 105
ggplot(data = newnexus) +
geom_bar(aes(x = SpendAtHome, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1) +
xlab("Time spent at home before pandemic") +
ylab("n")
ggplot(data = newnexus) +
geom_bar(aes(x = AmountOfTime, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1) +
xlab("Change in time spent at home after pandemic") +
ylab("Percentage of group")
# Page 108
ggplot(data = newnexus) +
geom_bar(aes(x = HoursDay, fill = Country)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1) +
scale_x_continuous(breaks=seq(0,24,1)) +
xlab("Hours of electricity per day") +
ylab("n")
ggplot(data = newnexus) +
geom_bar(aes(x = TemperatureInsideHome, fill = TemperatureInsideHome)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = TemperatureInsideHomeCat, fill = TemperatureInsideHomeCat)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
geom_bar(aes(x = WaterIntake)) +
scale_y_continuous(labels = scales::percent)
ggplot(data = nexusdata) +
geom_point(aes(x = Temperature, y = Relative_Humidity, colour = City, size = Heat_Index)) +
scale_size_binned(n.breaks = 8)
ggplot(data = nexusdata) +
geom_point(aes(x = Temperature, y = Relative_Humidity, colour = Country, size = Heat_Index)) +
scale_size_binned(n.breaks = 8)
ggplot(data = newnexus) +
geom_jitter(aes(x = Temperature, y = HoursDay, colour = City))
ggplot(data = newnexus) +
geom_bar(aes(x = Rooms, fill = City), position = "dodge")
ggplot(data = newnexus) +
geom_bar(aes(x = People_in_House, fill = City), position = "dodge")
ggplot(data = newnexus) +
geom_bar(aes(x = People_in_House, fill = factor(Rooms)), position = "dodge") +
scale_x_continuous(n.breaks = 20)
ggplot(data = newnexus) +
geom_bar(aes(x = People_in_House, fill = factor(Rooms)), position = "dodge") +
facet_grid(rows = vars(Country)) +
scale_x_continuous(n.breaks = 20)
ggplot(data = newnexus) +
geom_bar(aes(x = TemperatureInsideHome, fill = TemperatureOutsideHome)) +
facet_grid(rows = vars(TemperatureOutsideHome))
ggplot(data = newnexus) +
geom_bar(aes(x = Temperature, fill = TemperatureOutsideHome)) +
facet_grid(rows = vars(TemperatureOutsideHome))
ggplot(data = newnexus) +
geom_bar(aes(x = Temperature, fill = TemperatureInsideHome)) +
facet_grid(rows = vars(TemperatureInsideHome))
ggplot(data = nexusdata) +
geom_bar(aes(x = SpendAtHome)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = SpendAtHome, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = SpendAtHome, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = AmountOfTime)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = AmountOfTime, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = AmountOfTime, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = Income)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = Income, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = Income, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = EatingHabits)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = EatingHabits, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = EatingHabits, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = WaterIntake)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = WaterIntake, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = WaterIntake, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = ElectricityUsage)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = ElectricityUsage, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = ElectricityUsage, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = HealthServices)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = HealthServices, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = HealthServices, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = HomeWarm)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = HomeWarm, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = HomeWarm, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = HomeWarm)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
facet_grid(rows = vars(Country))
ggplot(data = nexusdata) +
geom_bar(aes(x = TemperatureInsideHome)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = TemperatureInsideHome, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = TemperatureInsideHome, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = TemperatureOutsideHome)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = TemperatureOutsideHome, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = nexusdata) +
geom_bar(aes(x = TemperatureOutsideHome, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = WallMaterial)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = WallMaterial)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = wallmass)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = walltherm)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = RoofingMaterial)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = RoofingMaterial)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = roofmass)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = rooftherm)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = wallmass, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = wallmass, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = roofmass)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = roofmass, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = roofmass, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = walltherm)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = walltherm, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = walltherm, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = rooftherm)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = rooftherm, fill = City), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = rooftherm, fill = Country), position = "dodge") +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = Temperature, fill = rooftherm)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = Windows, fill = rooftherm)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = Windows, fill = walltherm)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus %>% filter(TemperatureInsideHome!="don't know"&TemperatureInsideHome!="refused")) +
geom_bar(aes(x = Windows, fill = TemperatureInsideHome)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus %>% filter(TemperatureInsideHome!="don't know"&TemperatureInsideHome!="refused")) +
geom_bar(aes(x = Rooms, fill = TemperatureInsideHome)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus %>% filter(TemperatureInsideHome!="don't know"&TemperatureInsideHome!="refused")) +
geom_bar(aes(x = Windows, fill = Country), position = "dodge") +
facet_grid(rows = vars(TemperatureInsideHome))
ggplot(data = newnexus) +
geom_bar(aes(x = wallmass, fill = roofmass))
ggplot(data = newnexus) +
geom_bar(aes(x = wallmass, fill = roofmass)) +
facet_grid(rows = vars(Country))
ggplot(data = newnexus) +
geom_bar(aes(x = walltherm, fill = rooftherm)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
ggplot(data = newnexus) +
geom_bar(aes(x = walltherm, fill = rooftherm)) +
facet_grid(rows = vars(Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10))
##########################
ggplot(data = nexusdata) +
geom_violin(aes(x = City, y = HoursDay))
ggplot(data = nexusdata) +
geom_histogram(aes(x = HoursDay, fill = City), binwidth = 2) +
facet_grid(rows = vars(City))
ggplot(data = nexusdata) +
geom_histogram(aes(x = People_in_House, fill = City), binwidth = 2) +
facet_grid(rows = vars(City))
ggplot(data = nexusdata) +
geom_bar(aes(x = Windows, fill = City)) +
facet_grid(rows = vars(City))
Table 1 creation
Characteristics of the population
library(table1)
nexusdata2<-nexusdata
fac_cols <- sapply(nexusdata, is.character)
nexusdata2[fac_cols] <- lapply(nexusdata2[fac_cols], as.factor)
#Use the table1 command to produce a table 1
t1<-table1(~ Gender + Occupation_Coded + Language | Country, nexusdata2)
t1| Pakistan (N=1159) |
India (N=1180) |
Indonesia (N=1105) |
Cameroon (N=1101) |
Overall (N=4545) |
|
|---|---|---|---|---|---|
| Gender | |||||
| Female | 554 (47.8%) | 431 (36.5%) | 541 (49.0%) | 386 (35.1%) | 1912 (42.1%) |
| Male | 605 (52.2%) | 749 (63.5%) | 564 (51.0%) | 715 (64.9%) | 2633 (57.9%) |
| Occupation_Coded | |||||
| Unemployed | 33 (2.8%) | 0 (0%) | 68 (6.2%) | 81 (7.4%) | 182 (4.0%) |
| Government work | 43 (3.7%) | 1 (0.1%) | 12 (1.1%) | 12 (1.1%) | 68 (1.5%) |
| Informal trade / street sales | 45 (3.9%) | 618 (52.4%) | 33 (3.0%) | 290 (26.3%) | 986 (21.7%) |
| Business/self-employed | 150 (12.9%) | 154 (13.1%) | 319 (28.9%) | 210 (19.1%) | 833 (18.3%) |
| Paid labor/wage | 119 (10.3%) | 317 (26.9%) | 132 (11.9%) | 221 (20.1%) | 789 (17.4%) |
| Student | 205 (17.7%) | 0 (0%) | 16 (1.4%) | 22 (2.0%) | 243 (5.3%) |
| Agriculture/husbandry/fishing | 26 (2.2%) | 14 (1.2%) | 6 (0.5%) | 131 (11.9%) | 177 (3.9%) |
| Employed in private company | 165 (14.2%) | 26 (2.2%) | 253 (22.9%) | 64 (5.8%) | 508 (11.2%) |
| Help from family and/or friends [from inside of the country] | 3 (0.3%) | 26 (2.2%) | 0 (0%) | 27 (2.5%) | 56 (1.2%) |
| Housewife | 0 (0%) | 0 (0%) | 0 (0%) | 34 (3.1%) | 34 (0.7%) |
| Government assistance/social protection networks [pensions] | 8 (0.7%) | 3 (0.3%) | 0 (0%) | 8 (0.7%) | 19 (0.4%) |
| Migrant remittances [from outside of the country] | 1 (0.1%) | 21 (1.8%) | 0 (0%) | 1 (0.1%) | 23 (0.5%) |
| Housewife/husband/homemaker | 288 (24.8%) | 0 (0%) | 258 (23.3%) | 0 (0%) | 546 (12.0%) |
| Don't know | 26 (2.2%) | 0 (0%) | 6 (0.5%) | 0 (0%) | 32 (0.7%) |
| Refused | 47 (4.1%) | 0 (0%) | 1 (0.1%) | 0 (0%) | 48 (1.1%) |
| Government assistance / social protection networks [pensions] | 0 (0%) | 0 (0%) | 1 (0.1%) | 0 (0%) | 1 (0.0%) |
| Language | |||||
| French | 0 (0%) | 0 (0%) | 0 (0%) | 1072 (97.4%) | 1072 (23.6%) |
| English | 0 (0%) | 1 (0.1%) | 0 (0%) | 29 (2.6%) | 30 (0.7%) |
| Urdu | 1159 (100%) | 0 (0%) | 0 (0%) | 0 (0%) | 1159 (25.5%) |
| Hindi | 0 (0%) | 1179 (99.9%) | 0 (0%) | 0 (0%) | 1179 (25.9%) |
| Missing | 0 (0%) | 0 (0%) | 1105 (100%) | 0 (0%) | 1105 (24.3%) |
Housing materials and windows
t2<-table1(~ wallmass + roofmass + walltherm + rooftherm + Windows | Country, data = newnexus)
t2| Pakistan (N=1159) |
India (N=1180) |
Indonesia (N=1105) |
Cameroon (N=1101) |
Overall (N=4545) |
|
|---|---|---|---|---|---|
| wallmass | |||||
| Lightweight | 16 (1.4%) | 643 (54.5%) | 53 (4.8%) | 240 (21.8%) | 952 (20.9%) |
| Heavyweight | 1117 (96.4%) | 535 (45.3%) | 1036 (93.8%) | 861 (78.2%) | 3549 (78.1%) |
| Don't know | 18 (1.6%) | 2 (0.2%) | 16 (1.4%) | 0 (0%) | 36 (0.8%) |
| Refused | 8 (0.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 8 (0.2%) |
| roofmass | |||||
| Lightweight | 239 (20.6%) | 1086 (92.0%) | 688 (62.3%) | 706 (64.1%) | 2719 (59.8%) |
| Heavyweight | 896 (77.3%) | 94 (8.0%) | 404 (36.6%) | 395 (35.9%) | 1789 (39.4%) |
| Don't know | 14 (1.2%) | 0 (0%) | 13 (1.2%) | 0 (0%) | 27 (0.6%) |
| Refused | 10 (0.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 10 (0.2%) |
| walltherm | |||||
| Lightweight low thermal conductivity | 16 (1.4%) | 643 (54.5%) | 53 (4.8%) | 240 (21.8%) | 952 (20.9%) |
| Heavyweight low thermal conductivity | 442 (38.1%) | 467 (39.6%) | 651 (58.9%) | 168 (15.3%) | 1728 (38.0%) |
| Heavyweight high thermal conductivity | 675 (58.2%) | 68 (5.8%) | 385 (34.8%) | 693 (62.9%) | 1821 (40.1%) |
| Don't know | 18 (1.6%) | 2 (0.2%) | 16 (1.4%) | 0 (0%) | 36 (0.8%) |
| Refused | 8 (0.7%) | 0 (0%) | 0 (0%) | 0 (0%) | 8 (0.2%) |
| rooftherm | |||||
| Lightweight low thermal conductivity | 44 (3.8%) | 358 (30.3%) | 60 (5.4%) | 5 (0.5%) | 467 (10.3%) |
| Lightweight high thermal conductivity | 195 (16.8%) | 728 (61.7%) | 628 (56.8%) | 701 (63.7%) | 2252 (49.5%) |
| Heavyweight high thermal conductivity | 809 (69.8%) | 60 (5.1%) | 34 (3.1%) | 395 (35.9%) | 1298 (28.6%) |
| Heavyweight low thermal conductivity | 87 (7.5%) | 34 (2.9%) | 370 (33.5%) | 0 (0%) | 491 (10.8%) |
| Don't know | 14 (1.2%) | 0 (0%) | 13 (1.2%) | 0 (0%) | 27 (0.6%) |
| Refused | 10 (0.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 10 (0.2%) |
| Windows | |||||
| 0 | 3 (0.3%) | 14 (1.2%) | 3 (0.3%) | 2 (0.2%) | 22 (0.5%) |
| 1 | 262 (22.6%) | 527 (44.7%) | 129 (11.7%) | 99 (9.0%) | 1017 (22.4%) |
| 2 | 688 (59.4%) | 613 (51.9%) | 385 (34.8%) | 403 (36.6%) | 2089 (46.0%) |
| 3 | 161 (13.9%) | 19 (1.6%) | 298 (27.0%) | 283 (25.7%) | 761 (16.7%) |
| 4 | 27 (2.3%) | 7 (0.6%) | 151 (13.7%) | 152 (13.8%) | 337 (7.4%) |
| 5 | 5 (0.4%) | 0 (0%) | 44 (4.0%) | 102 (9.3%) | 151 (3.3%) |
| 6 | 11 (0.9%) | 0 (0%) | 43 (3.9%) | 58 (5.3%) | 112 (2.5%) |
| 7 | 1 (0.1%) | 0 (0%) | 10 (0.9%) | 2 (0.2%) | 13 (0.3%) |
| 8 | 0 (0%) | 0 (0%) | 18 (1.6%) | 0 (0%) | 18 (0.4%) |
| 9 | 1 (0.1%) | 0 (0%) | 4 (0.4%) | 0 (0%) | 5 (0.1%) |
| 10 | 0 (0%) | 0 (0%) | 20 (1.8%) | 0 (0%) | 20 (0.4%) |
Regression analysis
WallTherm
Pakistan
Standard model
library(tidyverse)
library(jtools)
library(kableExtra)
library(tidyverse)
newnexus<-filter(newnexus, walltherm != "Don't know") %>%
filter(walltherm != "Refused") %>%
filter(rooftherm != "Refused") %>%
filter(rooftherm != "Don't know") %>%
mutate(TemperatureInsideHomeCat = recode(TemperatureInsideHome,
"Very hot" = "Hot",
"Hot" = "Hot",
"Warm" = "Hot",
"Slightly warm" = "Hot",
"Comfortable" = "Cold",
"Slightly cool" = "Cold",
"Cool" = "Cold",
"Cold" = "Cold"))
newnexus$TemperatureInsideHome <- relevel(newnexus$TemperatureInsideHome, ref = "Cold")
newnexus<-newnexus %>%
filter(TemperatureInsideHomeCat != "Don't know") %>%
filter(TemperatureInsideHomeCat != "Refused")
unique(newnexus$TemperatureInsideHomeCat)## [1] Cold Hot
## Levels: Hot Cold Don't know Refusedsummary(newnexus$TemperatureInsideHomeCat)## Hot Cold Don't know Refused
## 3102 1343 0 0newnexus <- mutate(newnexus, NormallyDo_Coded = recode(NormallyDo_Coded,
"Do nothing specific" = "Do nothing",
"Go outside + Sit under tree" = "Go outside",
"Unclear" = "Do nothing",
"Use manual fan/cooling device" = "Use fan",
"Use cooler" = "Add ventilation",
"Drink cold drink" = "Drink something",
"Drink water" = "Drink something",
"Undress" = "Other",
"Go outside + Use fan" = "Use fan",
"Add ventilation + Go outside" = "Add ventilation",
"Take bath/shower + Use fan" = "Use fan",
"Take bath/shower + Go outside" = "Go outside"
))
newnexus<-filter(newnexus, NormallyDo_Coded == "Do nothing" | NormallyDo_Coded == "Go outside" | NormallyDo_Coded == "Use fan" | NormallyDo_Coded =="Add ventilation"| NormallyDo_Coded =="Drink something" | NormallyDo_Coded =="Take bath/shower")
newnexus<-newnexus %>%
mutate(floortherm = recode(FloorMaterial,
"ASPHALTED FLOOR" = "Heavyweight high thermal conductivity",
"CEMENT" = "Heavyweight high thermal conductivity",
"CERAMIC TILES" = "Heavyweight low thermal conductivity",
"CLAY COURT" = "Heavyweight low thermal conductivity",
"OTHER" = "Other",
"SAND/DIRT" = "Lightweight low thermal conductivity",
"WOOD/BAMBOO" = "Lightweight low thermal conductivity",
"CLAY COAT" = "Heavyweight low thermal conductivity",
"DON'T KNOW" = "Other"))
test<- newnexus %>%
group_by(NormallyDo_Coded) %>%
count() %>%
arrange(-n)
kable(test)| NormallyDo_Coded | n |
|---|---|
| Use fan | 1639 |
| Go outside | 903 |
| Add ventilation | 742 |
| Do nothing | 419 |
| Take bath/shower | 325 |
| Drink something | 76 |
PakistanModel<-filter(newnexus, Country == "Pakistan")
Model1 <- glm(TemperatureInsideHomeCat ~ walltherm, data = PakistanModel, family = binomial())
summ(Model1)| Observations | 997 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(2) | 1.25 |
| Pseudo-R² (Cragg-Uhler) | 0.00 |
| Pseudo-R² (McFadden) | 0.00 |
| AIC | 599.97 |
| BIC | 614.69 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | -2.64 | 1.04 | -2.55 | 0.01 |
| wallthermHeavyweight low thermal conductivity | 0.15 | 1.05 | 0.14 | 0.89 |
| wallthermHeavyweight high thermal conductivity | 0.40 | 1.04 | 0.38 | 0.70 |
| Standard errors: MLE |
Standard model + cooling practices + temperature outside home + Windows + Electricity + Water
Model5 <- glm(TemperatureInsideHomeCat ~ walltherm + TemperatureOutsideHome + Electricity + DrinkingWater + Gender
, data = PakistanModel, family = binomial())
summ(Model5)| Observations | 997 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(16) | 99.99 |
| Pseudo-R² (Cragg-Uhler) | 0.21 |
| Pseudo-R² (McFadden) | 0.17 |
| AIC | 529.23 |
| BIC | 612.61 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | -4.87 | 1.25 | -3.89 | 0.00 |
| wallthermHeavyweight low thermal conductivity | 0.32 | 1.13 | 0.28 | 0.78 |
| wallthermHeavyweight high thermal conductivity | 0.54 | 1.13 | 0.47 | 0.64 |
| TemperatureOutsideHomeVery difficult to tolerate | 0.57 | 0.54 | 1.05 | 0.29 |
| TemperatureOutsideHomeFairly difficult to tolerate | 2.03 | 0.46 | 4.37 | 0.00 |
| TemperatureOutsideHomeSlightly difficult to tolerate | 2.08 | 0.48 | 4.34 | 0.00 |
| TemperatureOutsideHomePerfectly tolerable | 3.41 | 0.53 | 6.48 | 0.00 |
| TemperatureOutsideHomeDon’t know | 4.87 | 1.32 | 3.69 | 0.00 |
| TemperatureOutsideHomeRefused | 2.37 | 4043.07 | 0.00 | 1.00 |
| ElectricityGenerator | 0.60 | 1.10 | 0.54 | 0.59 |
| Electricity12-volt battery | 1.33 | 0.43 | 3.09 | 0.00 |
| ElectricityInformal connection | 0.75 | 0.33 | 2.31 | 0.02 |
| ElectricityOther | -15.03 | 833.71 | -0.02 | 0.99 |
| DrinkingWatershared tap | -0.31 | 0.28 | -1.09 | 0.28 |
| DrinkingWatersupplied in tanks | -0.58 | 0.32 | -1.81 | 0.07 |
| DrinkingWaterother | -1.96 | 1.15 | -1.70 | 0.09 |
| GenderMale | 0.29 | 0.25 | 1.19 | 0.24 |
| Standard errors: MLE |
India
Standard model
IndiaModel<-filter(newnexus, Country == "India")
Model2 <- glm(TemperatureInsideHomeCat ~ walltherm, data = IndiaModel, family = binomial())
summ(Model2)| Observations | 1156 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(2) | 0.03 |
| Pseudo-R² (Cragg-Uhler) | 0.00 |
| Pseudo-R² (McFadden) | 0.00 |
| AIC | 1321.25 |
| BIC | 1336.40 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | -1.08 | 0.09 | -11.75 | 0.00 |
| wallthermHeavyweight low thermal conductivity | 0.02 | 0.14 | 0.16 | 0.87 |
| wallthermHeavyweight high thermal conductivity | -0.00 | 0.30 | -0.01 | 0.99 |
| Standard errors: MLE |
Standard model + cooling practices + temperature outside home
Model5 <- glm(TemperatureInsideHomeCat ~ walltherm + NormallyDo_Coded + TemperatureOutsideHome + DrinkingWater + Gender + SpendAtHome + Windows , data = IndiaModel, family = binomial())
summ(Model5)| Observations | 1156 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(22) | 320.67 |
| Pseudo-R² (Cragg-Uhler) | 0.36 |
| Pseudo-R² (McFadden) | 0.24 |
| AIC | 1040.61 |
| BIC | 1156.82 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | -1.14 | 1.79 | -0.64 | 0.52 |
| wallthermHeavyweight low thermal conductivity | -0.16 | 0.17 | -0.96 | 0.34 |
| wallthermHeavyweight high thermal conductivity | 0.33 | 0.37 | 0.91 | 0.36 |
| NormallyDo_CodedDo nothing | -14.62 | 1037.55 | -0.01 | 0.99 |
| NormallyDo_CodedGo outside | 0.16 | 0.35 | 0.46 | 0.65 |
| NormallyDo_CodedUse fan | 0.62 | 0.34 | 1.82 | 0.07 |
| TemperatureOutsideHomeVery difficult to tolerate | 0.31 | 0.42 | 0.74 | 0.46 |
| TemperatureOutsideHomeFairly difficult to tolerate | -0.90 | 0.41 | -2.19 | 0.03 |
| TemperatureOutsideHomeSlightly difficult to tolerate | -1.72 | 0.42 | -4.07 | 0.00 |
| TemperatureOutsideHomePerfectly tolerable | -2.17 | 0.72 | -3.03 | 0.00 |
| TemperatureOutsideHomeDon’t know | -0.68 | 1.22 | -0.56 | 0.58 |
| DrinkingWatershared tap | -0.44 | 1.55 | -0.29 | 0.77 |
| DrinkingWatersupplied in tanks | 0.09 | 1.55 | 0.06 | 0.95 |
| DrinkingWaterother | -0.38 | 2.11 | -0.18 | 0.86 |
| GenderMale | -0.02 | 0.17 | -0.10 | 0.92 |
| SpendAtHomeSome of the day | -0.47 | 0.30 | -1.55 | 0.12 |
| SpendAtHomeOther | -15.92 | 638.54 | -0.02 | 0.98 |
| SpendAtHomeVery little – only for sleeping | -1.22 | 0.23 | -5.28 | 0.00 |
| SpendAtHomeMost of the day – except for short trips out | -0.18 | 0.40 | -0.46 | 0.65 |
| Windows1 | 0.95 | 0.84 | 1.12 | 0.26 |
| Windows2 | 1.04 | 0.84 | 1.23 | 0.22 |
| Windows3 | 1.70 | 1.01 | 1.68 | 0.09 |
| Windows4 | 1.69 | 1.27 | 1.32 | 0.19 |
| Standard errors: MLE |
Indonesia
Standard model
IndonesiaModel<-filter(newnexus, Country == "Indonesia")
Model3 <- glm(TemperatureInsideHomeCat ~ walltherm, data = IndonesiaModel, family = binomial())
summ(Model3)| Observations | 903 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(2) | 5.67 |
| Pseudo-R² (Cragg-Uhler) | 0.01 |
| Pseudo-R² (McFadden) | 0.01 |
| AIC | 1120.14 |
| BIC | 1134.55 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | -0.23 | 0.31 | -0.76 | 0.45 |
| wallthermHeavyweight low thermal conductivity | -0.51 | 0.32 | -1.58 | 0.11 |
| wallthermHeavyweight high thermal conductivity | -0.74 | 0.33 | -2.23 | 0.03 |
| Standard errors: MLE |
Standard model + cooling practices + temperature outside home
Model5 <- glm(TemperatureInsideHomeCat ~ walltherm + NormallyDo_Coded + TemperatureOutsideHome + Gender + Rooms + Windows , data = IndonesiaModel, family = binomial())
summ(Model5)| Observations | 903 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(25) | 69.28 |
| Pseudo-R² (Cragg-Uhler) | 0.10 |
| Pseudo-R² (McFadden) | 0.06 |
| AIC | 1102.52 |
| BIC | 1227.47 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | -0.13 | 1.36 | -0.10 | 0.92 |
| wallthermHeavyweight low thermal conductivity | -0.65 | 0.34 | -1.91 | 0.06 |
| wallthermHeavyweight high thermal conductivity | -0.94 | 0.35 | -2.66 | 0.01 |
| NormallyDo_CodedDo nothing | 0.42 | 0.71 | 0.59 | 0.56 |
| NormallyDo_CodedDrink something | -13.19 | 502.88 | -0.03 | 0.98 |
| NormallyDo_CodedGo outside | -0.09 | 0.28 | -0.31 | 0.76 |
| NormallyDo_CodedTake bath/shower | 0.41 | 0.34 | 1.19 | 0.23 |
| NormallyDo_CodedUse fan | 0.28 | 0.21 | 1.35 | 0.18 |
| TemperatureOutsideHomeVery difficult to tolerate | -0.25 | 0.33 | -0.76 | 0.45 |
| TemperatureOutsideHomeFairly difficult to tolerate | -0.58 | 0.30 | -1.91 | 0.06 |
| TemperatureOutsideHomeSlightly difficult to tolerate | -0.24 | 0.29 | -0.84 | 0.40 |
| TemperatureOutsideHomePerfectly tolerable | 0.43 | 0.27 | 1.58 | 0.11 |
| TemperatureOutsideHomeDon’t know | 0.80 | 0.56 | 1.42 | 0.16 |
| TemperatureOutsideHomeRefused | -13.50 | 882.74 | -0.02 | 0.99 |
| GenderMale | 0.16 | 0.15 | 1.04 | 0.30 |
| Rooms | -0.15 | 0.13 | -1.21 | 0.23 |
| Windows1 | -0.12 | 1.32 | -0.09 | 0.93 |
| Windows2 | -0.21 | 1.31 | -0.16 | 0.87 |
| Windows3 | 0.23 | 1.31 | 0.17 | 0.86 |
| Windows4 | 0.28 | 1.31 | 0.22 | 0.83 |
| Windows5 | 0.66 | 1.35 | 0.49 | 0.62 |
| Windows6 | 1.29 | 1.35 | 0.96 | 0.34 |
| Windows7 | 2.01 | 1.58 | 1.27 | 0.20 |
| Windows8 | -0.81 | 1.52 | -0.53 | 0.59 |
| Windows9 | 15.80 | 622.04 | 0.03 | 0.98 |
| Windows10 | -0.32 | 1.45 | -0.22 | 0.82 |
| Standard errors: MLE |
Cameroon
Standard Model
CameroonModel<-filter(newnexus, Country == "Cameroon")
Model4 <- glm(TemperatureInsideHomeCat ~ walltherm, data = CameroonModel, family = binomial())
summ(Model4)| Observations | 1048 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(2) | 16.71 |
| Pseudo-R² (Cragg-Uhler) | 0.02 |
| Pseudo-R² (McFadden) | 0.01 |
| AIC | 1427.89 |
| BIC | 1442.76 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.25 | 0.13 | 1.91 | 0.06 |
| wallthermHeavyweight low thermal conductivity | -0.62 | 0.21 | -2.94 | 0.00 |
| wallthermHeavyweight high thermal conductivity | 0.11 | 0.15 | 0.74 | 0.46 |
| Standard errors: MLE |
Standard model + cooling practices + temperature outside home + Windows + Electricity + Water + Time at home
Model5 <- glm(TemperatureInsideHomeCat ~ walltherm + NormallyDo_Coded + TemperatureOutsideHome + Electricity + DrinkingWater + Gender + SpendAtHome, data = CameroonModel, family = binomial())
summ(Model5)| Observations | 1048 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(20) | 529.23 |
| Pseudo-R² (Cragg-Uhler) | 0.53 |
| Pseudo-R² (McFadden) | 0.37 |
| AIC | 951.37 |
| BIC | 1055.42 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | -3.03 | 1.66 | -1.83 | 0.07 |
| wallthermHeavyweight low thermal conductivity | 0.16 | 0.29 | 0.53 | 0.59 |
| wallthermHeavyweight high thermal conductivity | 0.19 | 0.22 | 0.88 | 0.38 |
| NormallyDo_CodedDo nothing | 1.84 | 0.24 | 7.55 | 0.00 |
| NormallyDo_CodedDrink something | 3.17 | 1.12 | 2.83 | 0.00 |
| NormallyDo_CodedGo outside | 2.96 | 0.30 | 10.01 | 0.00 |
| NormallyDo_CodedTake bath/shower | 1.80 | 0.36 | 5.03 | 0.00 |
| NormallyDo_CodedUse fan | 2.82 | 0.25 | 11.16 | 0.00 |
| TemperatureOutsideHomeVery difficult to tolerate | 0.85 | 0.48 | 1.77 | 0.08 |
| TemperatureOutsideHomeFairly difficult to tolerate | 2.07 | 0.44 | 4.70 | 0.00 |
| TemperatureOutsideHomeSlightly difficult to tolerate | 1.96 | 0.43 | 4.59 | 0.00 |
| TemperatureOutsideHomePerfectly tolerable | 3.99 | 0.47 | 8.46 | 0.00 |
| ElectricityInformal connection | 0.50 | 1.54 | 0.33 | 0.74 |
| ElectricityOther | 0.57 | 1.56 | 0.36 | 0.72 |
| DrinkingWatershared tap | -0.25 | 0.43 | -0.58 | 0.56 |
| DrinkingWatersupplied in tanks | 0.23 | 0.72 | 0.32 | 0.75 |
| GenderMale | -0.08 | 0.18 | -0.43 | 0.66 |
| SpendAtHomeSome of the day | -1.43 | 0.28 | -5.03 | 0.00 |
| SpendAtHomeOther | 0.46 | 1.04 | 0.44 | 0.66 |
| SpendAtHomeVery little – only for sleeping | -0.18 | 0.27 | -0.66 | 0.51 |
| SpendAtHomeMost of the day – except for short trips out | -0.91 | 0.32 | -2.87 | 0.00 |
| Standard errors: MLE |
RoofTherm
Pakistan
Standard model
library(tidyverse)
library(jtools)
newnexus<-filter(newnexus, walltherm != "Don't know") %>%
filter(walltherm != "Refused") %>%
filter(rooftherm != "Refused") %>%
filter(rooftherm != "Don't know") %>%
mutate(TemperatureInsideHomeCat = recode(TemperatureInsideHome,
"Very hot" = "Hot",
"Hot" = "Hot",
"Warm" = "Hot",
"Slightly warm" = "Hot",
"Comfortable" = "Cold",
"Slightly cool" = "Cold",
"Cool" = "Cold",
"Cold" = "Cold"))
newnexus$TemperatureInsideHome <- relevel(newnexus$TemperatureInsideHome, ref = "Cold")
newnexus<-newnexus %>%
filter(TemperatureInsideHomeCat != "Don't know") %>%
filter(TemperatureInsideHomeCat != "Refused")
PakistanModel<-filter(newnexus, Country == "Pakistan")
Model1 <- glm(TemperatureInsideHomeCat ~ rooftherm, data = PakistanModel, family = binomial())
summ(Model1)| Observations | 997 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(3) | 4.44 |
| Pseudo-R² (Cragg-Uhler) | 0.01 |
| Pseudo-R² (McFadden) | 0.01 |
| AIC | 598.77 |
| BIC | 618.39 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 2.97 | 0.72 | 4.10 | 0.00 |
| roofthermLightweight high thermal conductivity | -0.61 | 0.77 | -0.79 | 0.43 |
| roofthermHeavyweight high thermal conductivity | -0.58 | 0.74 | -0.79 | 0.43 |
| roofthermHeavyweight low thermal conductivity | -1.25 | 0.79 | -1.58 | 0.11 |
| Standard errors: MLE |
Standard model + 529
Model10 <- glm(TemperatureInsideHomeCat ~ rooftherm + TemperatureOutsideHome + Electricity + DrinkingWater + Gender + NormallyDo_Coded + People_in_House
, data = PakistanModel, family = binomial())
summ(Model10)| Observations | 997 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(23) | 106.25 |
| Pseudo-R² (Cragg-Uhler) | 0.22 |
| Pseudo-R² (McFadden) | 0.18 |
| AIC | 536.96 |
| BIC | 654.68 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 4.96 | 1.20 | 4.15 | 0.00 |
| roofthermLightweight high thermal conductivity | -0.84 | 0.83 | -1.01 | 0.31 |
| roofthermHeavyweight high thermal conductivity | -0.56 | 0.78 | -0.72 | 0.47 |
| roofthermHeavyweight low thermal conductivity | -1.38 | 0.86 | -1.61 | 0.11 |
| TemperatureOutsideHomeVery difficult to tolerate | -0.56 | 0.54 | -1.03 | 0.31 |
| TemperatureOutsideHomeFairly difficult to tolerate | -2.08 | 0.47 | -4.46 | 0.00 |
| TemperatureOutsideHomeSlightly difficult to tolerate | -2.11 | 0.48 | -4.38 | 0.00 |
| TemperatureOutsideHomePerfectly tolerable | -3.52 | 0.53 | -6.60 | 0.00 |
| TemperatureOutsideHomeDon’t know | -4.58 | 1.33 | -3.46 | 0.00 |
| TemperatureOutsideHomeRefused | -2.23 | 4044.02 | -0.00 | 1.00 |
| ElectricityGenerator | -0.50 | 1.13 | -0.44 | 0.66 |
| Electricity12-volt battery | -1.15 | 0.43 | -2.68 | 0.01 |
| ElectricityInformal connection | -0.81 | 0.33 | -2.45 | 0.01 |
| ElectricityOther | 14.85 | 838.28 | 0.02 | 0.99 |
| DrinkingWatershared tap | 0.31 | 0.29 | 1.06 | 0.29 |
| DrinkingWatersupplied in tanks | 0.59 | 0.33 | 1.78 | 0.08 |
| DrinkingWaterother | 1.99 | 1.16 | 1.72 | 0.09 |
| GenderMale | -0.37 | 0.26 | -1.46 | 0.14 |
| NormallyDo_CodedDo nothing | -0.19 | 0.69 | -0.27 | 0.78 |
| NormallyDo_CodedDrink something | -0.37 | 0.81 | -0.46 | 0.65 |
| NormallyDo_CodedGo outside | 0.01 | 0.70 | 0.02 | 0.98 |
| NormallyDo_CodedTake bath/shower | -0.29 | 0.69 | -0.41 | 0.68 |
| NormallyDo_CodedUse fan | 0.00 | 0.69 | 0.01 | 1.00 |
| People_in_House | 0.05 | 0.06 | 0.98 | 0.33 |
| Standard errors: MLE |
India
standard model
IndiaModel<-filter(newnexus, Country == "India")
Model2 <- glm(TemperatureInsideHomeCat ~ rooftherm, data = IndiaModel, family = binomial())
summ(Model2)| Observations | 1156 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(3) | 1.29 |
| Pseudo-R² (Cragg-Uhler) | 0.00 |
| Pseudo-R² (McFadden) | 0.00 |
| AIC | 1321.98 |
| BIC | 1342.19 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 1.17 | 0.13 | 9.33 | 0.00 |
| roofthermLightweight high thermal conductivity | -0.16 | 0.15 | -1.03 | 0.30 |
| roofthermHeavyweight high thermal conductivity | -0.18 | 0.32 | -0.58 | 0.56 |
| roofthermHeavyweight low thermal conductivity | 0.06 | 0.45 | 0.13 | 0.89 |
| Standard errors: MLE |
standard model + 1041
Model11 <- glm(TemperatureInsideHomeCat ~ rooftherm + TemperatureOutsideHome + DrinkingWater + Gender + NormallyDo_Coded + SpendAtHome
, data = IndiaModel, family = binomial())
summ(Model11)| Observations | 1156 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(19) | 318.04 |
| Pseudo-R² (Cragg-Uhler) | 0.35 |
| Pseudo-R² (McFadden) | 0.24 |
| AIC | 1037.23 |
| BIC | 1138.29 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.50 | 1.57 | 0.31 | 0.75 |
| roofthermLightweight high thermal conductivity | -0.29 | 0.18 | -1.62 | 0.11 |
| roofthermHeavyweight high thermal conductivity | -0.53 | 0.38 | -1.39 | 0.16 |
| roofthermHeavyweight low thermal conductivity | -0.12 | 0.53 | -0.22 | 0.82 |
| TemperatureOutsideHomeVery difficult to tolerate | -0.39 | 0.42 | -0.93 | 0.35 |
| TemperatureOutsideHomeFairly difficult to tolerate | 0.81 | 0.42 | 1.94 | 0.05 |
| TemperatureOutsideHomeSlightly difficult to tolerate | 1.63 | 0.43 | 3.81 | 0.00 |
| TemperatureOutsideHomePerfectly tolerable | 2.15 | 0.72 | 2.99 | 0.00 |
| TemperatureOutsideHomeDon’t know | 0.63 | 1.23 | 0.51 | 0.61 |
| DrinkingWatershared tap | 0.32 | 1.52 | 0.21 | 0.83 |
| DrinkingWatersupplied in tanks | -0.24 | 1.52 | -0.16 | 0.87 |
| DrinkingWaterother | 0.28 | 2.10 | 0.13 | 0.90 |
| GenderMale | 0.05 | 0.17 | 0.29 | 0.77 |
| NormallyDo_CodedDo nothing | 14.82 | 1017.11 | 0.01 | 0.99 |
| NormallyDo_CodedGo outside | 0.01 | 0.34 | 0.04 | 0.97 |
| NormallyDo_CodedUse fan | -0.51 | 0.33 | -1.57 | 0.12 |
| SpendAtHomeSome of the day | 0.48 | 0.30 | 1.61 | 0.11 |
| SpendAtHomeOther | 15.95 | 642.33 | 0.02 | 0.98 |
| SpendAtHomeVery little – only for sleeping | 1.20 | 0.23 | 5.19 | 0.00 |
| SpendAtHomeMost of the day – except for short trips out | 0.16 | 0.40 | 0.40 | 0.69 |
| Standard errors: MLE |
Indonesia
standard model
IndonesiaModel<-filter(newnexus, Country == "Indonesia")
Model3 <- glm(TemperatureInsideHomeCat ~ rooftherm, data = IndonesiaModel, family = binomial())
summ(Model3)| Observations | 903 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(3) | 0.71 |
| Pseudo-R² (Cragg-Uhler) | 0.00 |
| Pseudo-R² (McFadden) | 0.00 |
| AIC | 1127.09 |
| BIC | 1146.32 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.98 | 0.34 | 2.90 | 0.00 |
| roofthermLightweight high thermal conductivity | -0.18 | 0.35 | -0.51 | 0.61 |
| roofthermHeavyweight high thermal conductivity | 0.02 | 0.56 | 0.03 | 0.97 |
| roofthermHeavyweight low thermal conductivity | -0.24 | 0.36 | -0.66 | 0.51 |
| Standard errors: MLE |
Standard model + 1102
Model12 <- glm(TemperatureInsideHomeCat ~ rooftherm + TemperatureOutsideHome + Electricity + DrinkingWater + Gender + NormallyDo_Coded + SpendAtHome + Windows + People_in_House
, data = IndonesiaModel, family = binomial())
summ(Model12)| Observations | 903 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(36) | 75.76 |
| Pseudo-R² (Cragg-Uhler) | 0.11 |
| Pseudo-R² (McFadden) | 0.07 |
| AIC | 1118.05 |
| BIC | 1295.86 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 15.71 | 707.49 | 0.02 | 0.98 |
| roofthermLightweight high thermal conductivity | -0.08 | 0.38 | -0.21 | 0.83 |
| roofthermHeavyweight high thermal conductivity | 0.13 | 0.60 | 0.21 | 0.83 |
| roofthermHeavyweight low thermal conductivity | -0.19 | 0.39 | -0.48 | 0.63 |
| TemperatureOutsideHomeVery difficult to tolerate | 0.12 | 0.33 | 0.35 | 0.72 |
| TemperatureOutsideHomeFairly difficult to tolerate | 0.51 | 0.31 | 1.65 | 0.10 |
| TemperatureOutsideHomeSlightly difficult to tolerate | 0.14 | 0.29 | 0.47 | 0.64 |
| TemperatureOutsideHomePerfectly tolerable | -0.54 | 0.28 | -1.96 | 0.05 |
| TemperatureOutsideHomeDon’t know | -0.89 | 0.57 | -1.56 | 0.12 |
| TemperatureOutsideHomeRefused | 14.42 | 1455.40 | 0.01 | 0.99 |
| ElectricityInformal connection | -14.78 | 707.49 | -0.02 | 0.98 |
| ElectricityOther | -14.89 | 707.49 | -0.02 | 0.98 |
| DrinkingWatershared tap | 0.15 | 0.23 | 0.66 | 0.51 |
| DrinkingWatersupplied in tanks | 0.48 | 0.90 | 0.53 | 0.60 |
| DrinkingWaterother | 0.34 | 0.23 | 1.50 | 0.13 |
| GenderMale | -0.15 | 0.16 | -0.94 | 0.35 |
| NormallyDo_CodedDo nothing | -0.49 | 0.71 | -0.69 | 0.49 |
| NormallyDo_CodedDrink something | 14.14 | 833.71 | 0.02 | 0.99 |
| NormallyDo_CodedGo outside | 0.08 | 0.29 | 0.28 | 0.78 |
| NormallyDo_CodedTake bath/shower | -0.30 | 0.35 | -0.88 | 0.38 |
| NormallyDo_CodedUse fan | -0.23 | 0.21 | -1.11 | 0.27 |
| SpendAtHomeSome of the day | -0.64 | 0.41 | -1.57 | 0.12 |
| SpendAtHomeOther | 1.39 | 1.11 | 1.25 | 0.21 |
| SpendAtHomeDon’t know | -15.36 | 1455.40 | -0.01 | 0.99 |
| SpendAtHomeVery little – only for sleeping | -0.04 | 0.20 | -0.20 | 0.84 |
| SpendAtHomeMost of the day – except for short trips out | 0.23 | 0.18 | 1.27 | 0.20 |
| Windows1 | 0.41 | 1.29 | 0.32 | 0.75 |
| Windows2 | 0.52 | 1.28 | 0.41 | 0.68 |
| Windows3 | 0.18 | 1.28 | 0.14 | 0.89 |
| Windows4 | 0.07 | 1.28 | 0.05 | 0.96 |
| Windows5 | -0.24 | 1.33 | -0.18 | 0.85 |
| Windows6 | -0.80 | 1.32 | -0.61 | 0.54 |
| Windows7 | -1.52 | 1.56 | -0.97 | 0.33 |
| Windows8 | 1.32 | 1.49 | 0.88 | 0.38 |
| Windows9 | -15.79 | 1024.22 | -0.02 | 0.99 |
| Windows10 | 0.83 | 1.42 | 0.58 | 0.56 |
| People_in_House | -0.03 | 0.05 | -0.54 | 0.59 |
| Standard errors: MLE |
Cameroon
standard model
CameroonModel<-filter(newnexus, Country == "Cameroon")
Model4 <- glm(TemperatureInsideHomeCat ~ rooftherm, data = CameroonModel, family = binomial())
summ(Model4)| Observations | 1048 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(2) | 1.37 |
| Pseudo-R² (Cragg-Uhler) | 0.00 |
| Pseudo-R² (McFadden) | 0.00 |
| AIC | 1443.23 |
| BIC | 1458.10 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.41 | 0.91 | 0.44 | 0.66 |
| roofthermLightweight high thermal conductivity | -0.60 | 0.92 | -0.65 | 0.51 |
| roofthermHeavyweight high thermal conductivity | -0.72 | 0.92 | -0.78 | 0.43 |
| Standard errors: MLE |
standard model + 950
Model13 <- glm(TemperatureInsideHomeCat ~ rooftherm + TemperatureOutsideHome + Gender + NormallyDo_Coded + SpendAtHome + Windows
, data = CameroonModel, family = binomial())
summ(Model13)| Observations | 1048 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(23) | 542.40 |
| Pseudo-R² (Cragg-Uhler) | 0.54 |
| Pseudo-R² (McFadden) | 0.38 |
| AIC | 944.20 |
| BIC | 1063.11 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 18.09 | 523.14 | 0.03 | 0.97 |
| roofthermLightweight high thermal conductivity | -0.98 | 1.15 | -0.85 | 0.39 |
| roofthermHeavyweight high thermal conductivity | -0.88 | 1.15 | -0.76 | 0.45 |
| TemperatureOutsideHomeVery difficult to tolerate | -0.83 | 0.49 | -1.70 | 0.09 |
| TemperatureOutsideHomeFairly difficult to tolerate | -2.08 | 0.45 | -4.63 | 0.00 |
| TemperatureOutsideHomeSlightly difficult to tolerate | -1.93 | 0.43 | -4.44 | 0.00 |
| TemperatureOutsideHomePerfectly tolerable | -3.99 | 0.48 | -8.34 | 0.00 |
| GenderMale | 0.11 | 0.18 | 0.59 | 0.55 |
| NormallyDo_CodedDo nothing | -1.57 | 0.31 | -5.15 | 0.00 |
| NormallyDo_CodedDrink something | -3.12 | 1.11 | -2.79 | 0.01 |
| NormallyDo_CodedGo outside | -3.03 | 0.29 | -10.42 | 0.00 |
| NormallyDo_CodedTake bath/shower | -1.83 | 0.36 | -5.13 | 0.00 |
| NormallyDo_CodedUse fan | -2.78 | 0.24 | -11.36 | 0.00 |
| SpendAtHomeSome of the day | 1.38 | 0.29 | 4.82 | 0.00 |
| SpendAtHomeOther | -0.55 | 1.07 | -0.51 | 0.61 |
| SpendAtHomeVery little – only for sleeping | 0.12 | 0.27 | 0.44 | 0.66 |
| SpendAtHomeMost of the day – except for short trips out | 0.80 | 0.32 | 2.49 | 0.01 |
| Windows1 | -14.69 | 523.14 | -0.03 | 0.98 |
| Windows2 | -14.31 | 523.14 | -0.03 | 0.98 |
| Windows3 | -14.48 | 523.14 | -0.03 | 0.98 |
| Windows4 | -14.63 | 523.14 | -0.03 | 0.98 |
| Windows5 | -15.02 | 523.14 | -0.03 | 0.98 |
| Windows6 | -15.42 | 523.14 | -0.03 | 0.98 |
| Windows7 | -28.06 | 809.99 | -0.03 | 0.97 |
| Standard errors: MLE |
FloorTherm
Pakistan
Model5 <- glm(TemperatureInsideHomeCat ~ floortherm + TemperatureOutsideHome + Electricity + DrinkingWater + Gender
, data = PakistanModel, family = binomial())
summ(Model5)| Observations | 997 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(17) | 104.15 |
| Pseudo-R² (Cragg-Uhler) | 0.22 |
| Pseudo-R² (McFadden) | 0.17 |
| AIC | 527.07 |
| BIC | 615.36 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 4.47 | 0.55 | 8.10 | 0.00 |
| floorthermHeavyweight low thermal conductivity | -0.19 | 0.32 | -0.59 | 0.55 |
| floorthermOther | 16.00 | 3956.18 | 0.00 | 1.00 |
| floorthermLightweight low thermal conductivity | 1.33 | 0.81 | 1.64 | 0.10 |
| TemperatureOutsideHomeVery difficult to tolerate | -0.63 | 0.54 | -1.17 | 0.24 |
| TemperatureOutsideHomeFairly difficult to tolerate | -2.08 | 0.46 | -4.49 | 0.00 |
| TemperatureOutsideHomeSlightly difficult to tolerate | -2.06 | 0.48 | -4.29 | 0.00 |
| TemperatureOutsideHomePerfectly tolerable | -3.45 | 0.52 | -6.58 | 0.00 |
| TemperatureOutsideHomeDon’t know | -5.16 | 1.39 | -3.72 | 0.00 |
| TemperatureOutsideHomeRefused | -2.63 | 4042.49 | -0.00 | 1.00 |
| ElectricityGenerator | -0.56 | 1.10 | -0.51 | 0.61 |
| Electricity12-volt battery | -1.18 | 0.42 | -2.81 | 0.00 |
| ElectricityInformal connection | -0.81 | 0.33 | -2.48 | 0.01 |
| ElectricityOther | 15.17 | 830.88 | 0.02 | 0.99 |
| DrinkingWatershared tap | 0.32 | 0.29 | 1.13 | 0.26 |
| DrinkingWatersupplied in tanks | 0.56 | 0.32 | 1.74 | 0.08 |
| DrinkingWaterother | 1.79 | 1.14 | 1.56 | 0.12 |
| GenderMale | -0.34 | 0.25 | -1.36 | 0.17 |
| Standard errors: MLE |
India
Standard model + cooling practices + temperature outside home
Model5 <- glm(TemperatureInsideHomeCat ~ floortherm + NormallyDo_Coded + TemperatureOutsideHome + DrinkingWater + Gender + SpendAtHome + Windows , data = IndiaModel, family = binomial())
summ(Model5)| Observations | 1156 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(23) | 318.95 |
| Pseudo-R² (Cragg-Uhler) | 0.35 |
| Pseudo-R² (McFadden) | 0.24 |
| AIC | 1044.33 |
| BIC | 1165.59 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 1.05 | 1.79 | 0.59 | 0.56 |
| floorthermHeavyweight low thermal conductivity | 0.01 | 0.18 | 0.05 | 0.96 |
| floorthermOther | 0.81 | 1.22 | 0.66 | 0.51 |
| floorthermLightweight low thermal conductivity | 0.04 | 0.28 | 0.15 | 0.88 |
| NormallyDo_CodedDo nothing | 14.67 | 1009.90 | 0.01 | 0.99 |
| NormallyDo_CodedGo outside | -0.04 | 0.34 | -0.11 | 0.91 |
| NormallyDo_CodedUse fan | -0.52 | 0.33 | -1.58 | 0.12 |
| TemperatureOutsideHomeVery difficult to tolerate | -0.34 | 0.42 | -0.81 | 0.42 |
| TemperatureOutsideHomeFairly difficult to tolerate | 0.86 | 0.41 | 2.09 | 0.04 |
| TemperatureOutsideHomeSlightly difficult to tolerate | 1.67 | 0.42 | 3.95 | 0.00 |
| TemperatureOutsideHomePerfectly tolerable | 2.12 | 0.71 | 2.97 | 0.00 |
| TemperatureOutsideHomeDon’t know | 0.70 | 1.23 | 0.57 | 0.57 |
| DrinkingWatershared tap | 0.49 | 1.55 | 0.32 | 0.75 |
| DrinkingWatersupplied in tanks | -0.02 | 1.55 | -0.01 | 0.99 |
| DrinkingWaterother | 0.44 | 2.12 | 0.21 | 0.84 |
| GenderMale | 0.01 | 0.17 | 0.09 | 0.93 |
| SpendAtHomeSome of the day | 0.47 | 0.30 | 1.58 | 0.11 |
| SpendAtHomeOther | 15.95 | 640.36 | 0.02 | 0.98 |
| SpendAtHomeVery little – only for sleeping | 1.22 | 0.23 | 5.30 | 0.00 |
| SpendAtHomeMost of the day – except for short trips out | 0.21 | 0.40 | 0.51 | 0.61 |
| Windows1 | -0.96 | 0.84 | -1.14 | 0.25 |
| Windows2 | -1.02 | 0.84 | -1.22 | 0.22 |
| Windows3 | -1.74 | 1.01 | -1.72 | 0.08 |
| Windows4 | -1.75 | 1.28 | -1.37 | 0.17 |
| Standard errors: MLE |
Indonesia
Standard model + cooling practices + temperature outside home
Model5 <- glm(TemperatureInsideHomeCat ~ floortherm + NormallyDo_Coded + TemperatureOutsideHome + Gender + Rooms + Windows , data = IndonesiaModel, family = binomial())
summ(Model5)| Observations | 903 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(26) | 63.62 |
| Pseudo-R² (Cragg-Uhler) | 0.10 |
| Pseudo-R² (McFadden) | 0.06 |
| AIC | 1110.18 |
| BIC | 1239.94 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 0.50 | 1.32 | 0.38 | 0.71 |
| floorthermHeavyweight low thermal conductivity | 0.32 | 0.22 | 1.46 | 0.14 |
| floorthermOther | 0.09 | 0.85 | 0.10 | 0.92 |
| floorthermLightweight low thermal conductivity | 0.58 | 0.75 | 0.77 | 0.44 |
| NormallyDo_CodedDo nothing | -0.50 | 0.70 | -0.72 | 0.47 |
| NormallyDo_CodedDrink something | 13.24 | 505.12 | 0.03 | 0.98 |
| NormallyDo_CodedGo outside | 0.06 | 0.28 | 0.21 | 0.84 |
| NormallyDo_CodedTake bath/shower | -0.41 | 0.34 | -1.19 | 0.24 |
| NormallyDo_CodedUse fan | -0.30 | 0.20 | -1.46 | 0.15 |
| TemperatureOutsideHomeVery difficult to tolerate | 0.21 | 0.33 | 0.64 | 0.52 |
| TemperatureOutsideHomeFairly difficult to tolerate | 0.57 | 0.30 | 1.89 | 0.06 |
| TemperatureOutsideHomeSlightly difficult to tolerate | 0.26 | 0.29 | 0.89 | 0.37 |
| TemperatureOutsideHomePerfectly tolerable | -0.45 | 0.27 | -1.66 | 0.10 |
| TemperatureOutsideHomeDon’t know | -0.81 | 0.56 | -1.44 | 0.15 |
| TemperatureOutsideHomeRefused | 13.64 | 882.74 | 0.02 | 0.99 |
| GenderMale | -0.20 | 0.15 | -1.28 | 0.20 |
| Rooms | 0.14 | 0.12 | 1.15 | 0.25 |
| Windows1 | 0.26 | 1.28 | 0.21 | 0.84 |
| Windows2 | 0.35 | 1.27 | 0.27 | 0.78 |
| Windows3 | -0.06 | 1.27 | -0.05 | 0.96 |
| Windows4 | -0.19 | 1.27 | -0.15 | 0.88 |
| Windows5 | -0.53 | 1.31 | -0.40 | 0.69 |
| Windows6 | -1.09 | 1.31 | -0.83 | 0.41 |
| Windows7 | -1.79 | 1.55 | -1.16 | 0.25 |
| Windows8 | 0.88 | 1.49 | 0.59 | 0.55 |
| Windows9 | -15.48 | 622.12 | -0.02 | 0.98 |
| Windows10 | 0.52 | 1.42 | 0.37 | 0.71 |
| Standard errors: MLE |
Cameroon
Standard model + cooling practices + temperature outside home + Windows + Electricity + Water + Time at home
Model5 <- glm(TemperatureInsideHomeCat ~ floortherm + NormallyDo_Coded + TemperatureOutsideHome + Electricity + DrinkingWater + Gender + SpendAtHome, data = CameroonModel, family = binomial())
summ(Model5)| Observations | 1048 |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(21) | 529.62 |
| Pseudo-R² (Cragg-Uhler) | 0.53 |
| Pseudo-R² (McFadden) | 0.37 |
| AIC | 952.98 |
| BIC | 1061.99 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | 2.61 | 1.92 | 1.36 | 0.17 |
| floorthermHeavyweight low thermal conductivity | 0.64 | 1.73 | 0.37 | 0.71 |
| floorthermOther | 11.58 | 535.41 | 0.02 | 0.98 |
| floorthermLightweight low thermal conductivity | 0.24 | 0.27 | 0.88 | 0.38 |
| NormallyDo_CodedDo nothing | -1.83 | 0.24 | -7.67 | 0.00 |
| NormallyDo_CodedDrink something | -3.12 | 1.12 | -2.79 | 0.01 |
| NormallyDo_CodedGo outside | -2.96 | 0.29 | -10.18 | 0.00 |
| NormallyDo_CodedTake bath/shower | -1.83 | 0.37 | -4.97 | 0.00 |
| NormallyDo_CodedUse fan | -2.74 | 0.25 | -11.07 | 0.00 |
| TemperatureOutsideHomeVery difficult to tolerate | -0.86 | 0.48 | -1.77 | 0.08 |
| TemperatureOutsideHomeFairly difficult to tolerate | -2.06 | 0.44 | -4.68 | 0.00 |
| TemperatureOutsideHomeSlightly difficult to tolerate | -1.96 | 0.43 | -4.58 | 0.00 |
| TemperatureOutsideHomePerfectly tolerable | -4.00 | 0.47 | -8.46 | 0.00 |
| ElectricityInformal connection | -0.28 | 1.82 | -0.15 | 0.88 |
| ElectricityOther | -0.34 | 1.84 | -0.18 | 0.85 |
| DrinkingWatershared tap | 0.26 | 0.42 | 0.62 | 0.53 |
| DrinkingWatersupplied in tanks | -0.24 | 0.72 | -0.33 | 0.74 |
| GenderMale | 0.07 | 0.18 | 0.40 | 0.69 |
| SpendAtHomeSome of the day | 1.43 | 0.28 | 5.04 | 0.00 |
| SpendAtHomeOther | -0.52 | 1.05 | -0.49 | 0.62 |
| SpendAtHomeVery little – only for sleeping | 0.18 | 0.27 | 0.68 | 0.49 |
| SpendAtHomeMost of the day – except for short trips out | 0.92 | 0.32 | 2.91 | 0.00 |
| Standard errors: MLE |
Diagram of variable relationships
library(knitr)
include_graphics("Variable_diagram.png")
Testing
library(stringr)
library(rstatix)
ggplot(data = newnexus) +
geom_bar(aes(x = rooftherm, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1) + theme(axis.text.x = element_text(angle = 45, vjust = 0.75))
newnexus %>%
group_by(Country, rooftherm) %>%
count()## # A tibble: 15 x 3
## # Groups: Country, rooftherm [15]
## Country rooftherm n
## <fct> <fct> <int>
## 1 Pakistan Lightweight low thermal conductivity 41
## 2 Pakistan Lightweight high thermal conductivity 186
## 3 Pakistan Heavyweight high thermal conductivity 691
## 4 Pakistan Heavyweight low thermal conductivity 79
## 5 India Lightweight low thermal conductivity 351
## 6 India Lightweight high thermal conductivity 715
## 7 India Heavyweight high thermal conductivity 59
## 8 India Heavyweight low thermal conductivity 31
## 9 Indonesia Lightweight low thermal conductivity 44
## 10 Indonesia Lightweight high thermal conductivity 510
## 11 Indonesia Heavyweight high thermal conductivity 26
## 12 Indonesia Heavyweight low thermal conductivity 323
## 13 Cameroon Lightweight low thermal conductivity 5
## 14 Cameroon Lightweight high thermal conductivity 664
## 15 Cameroon Heavyweight high thermal conductivity 379# Page 100
ggplot(data = newnexus) +
geom_bar(aes(x = walltherm, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1)
newnexus %>%
group_by(Country, walltherm) %>%
count()## # A tibble: 12 x 3
## # Groups: Country, walltherm [12]
## Country walltherm n
## <fct> <fct> <int>
## 1 Pakistan Lightweight low thermal conductivity 15
## 2 Pakistan Heavyweight low thermal conductivity 391
## 3 Pakistan Heavyweight high thermal conductivity 591
## 4 India Lightweight low thermal conductivity 629
## 5 India Heavyweight low thermal conductivity 460
## 6 India Heavyweight high thermal conductivity 67
## 7 Indonesia Lightweight low thermal conductivity 43
## 8 Indonesia Heavyweight low thermal conductivity 539
## 9 Indonesia Heavyweight high thermal conductivity 321
## 10 Cameroon Lightweight low thermal conductivity 229
## 11 Cameroon Heavyweight low thermal conductivity 156
## 12 Cameroon Heavyweight high thermal conductivity 663newnexus<- filter(newnexus, floortherm != "Other")
ggplot(data = newnexus) +
geom_bar(aes(x = floortherm, fill = Country)) +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
scale_fill_manual(values = cbPalette) +
facet_wrap(facets = vars(Country), ncol = 1) + theme(axis.text.x = element_text(angle = 45, vjust = 0.75))
newnexus %>%
group_by(Country, floortherm) %>%
count()## # A tibble: 12 x 3
## # Groups: Country, floortherm [12]
## Country floortherm n
## <fct> <fct> <int>
## 1 Pakistan Heavyweight high thermal conductivity 733
## 2 Pakistan Heavyweight low thermal conductivity 187
## 3 Pakistan Lightweight low thermal conductivity 76
## 4 India Heavyweight high thermal conductivity 533
## 5 India Heavyweight low thermal conductivity 500
## 6 India Lightweight low thermal conductivity 117
## 7 Indonesia Heavyweight high thermal conductivity 128
## 8 Indonesia Heavyweight low thermal conductivity 756
## 9 Indonesia Lightweight low thermal conductivity 10
## 10 Cameroon Heavyweight high thermal conductivity 904
## 11 Cameroon Heavyweight low thermal conductivity 3
## 12 Cameroon Lightweight low thermal conductivity 140cramer_v(newnexus$walltherm, newnexus$rooftherm)## [1] 0.3355939Model5 <- glm(TemperatureInsideHomeCat ~ Temperature, data = newnexus, family = binomial())
summ(Model5)| Observations | 3992 (95 missing obs. deleted) |
| Dependent variable | TemperatureInsideHomeCat |
| Type | Generalized linear model |
| Family | binomial |
| Link | logit |
| χ²(1) | 311.06 |
| Pseudo-R² (Cragg-Uhler) | 0.11 |
| Pseudo-R² (McFadden) | 0.06 |
| AIC | 4613.99 |
| BIC | 4626.58 |
| Est. | S.E. | z val. | p | |
|---|---|---|---|---|
| (Intercept) | -4.27 | 0.31 | -13.79 | 0.00 |
| Temperature | 0.16 | 0.01 | 16.24 | 0.00 |
| Standard errors: MLE |
---
title: "NexusData"
author: '680029924'
date: "21/01/2022"
output:
  html_document:
      toc: true
      toc_float: true
      toc_depth: 5
      code_folding: hide
      code_download: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```

```{r, cache = TRUE}
# Formatting chunk

# Clear workspace
rm(list = ls())

# Specify libraries

library(tidyverse)
library(lubridate)
library(naniar)

# Read data from web
cameroon_read <- read_csv(url("https://datashare.ed.ac.uk/bitstream/handle/10283/3804/Heat-Covid%20Nexus%20Survey_Cameroon.csv?sequence=1&isAllowed=y"), n_max = 2000)
indonesia_read <- read_csv(url("https://datashare.ed.ac.uk/bitstream/handle/10283/3804/Heat-Covid%20Nexus%20Survey_Indonesia.csv?sequence=2&isAllowed=y"), n_max = 2000)
pakistan_read <- read_csv(url("https://datashare.ed.ac.uk/bitstream/handle/10283/3804/Heat-Covid%20Nexus%20Survey_Pakistan.csv?sequence=14&isAllowed=y"), n_max = 2000)
india_read <- read_csv(url("https://datashare.ed.ac.uk/bitstream/handle/10283/3804/Heat-Covid%20Nexus%20Survey_India.csv?sequence=15&isAllowed=y"), n_max = 2000)


# Crop objects
pakistan_cropped <- pakistan_read %>% filter(Country == "Pakistan")


india_cropped <- india_read %>% filter(Country == "India")


# Generate R-legible names
names(cameroon_read) <- str_replace_all(names(cameroon_read), c(" " = "_", 
                                                                "/" = "_or_",
                                                                "'" = ""))
names(indonesia_read) <- str_replace_all(names(indonesia_read), c(" " = "_", 
                                                                  "/" = "_or_",
                                                                  "'" = ""))
names(pakistan_cropped) <- str_replace_all(names(pakistan_cropped), c(" " = "_", 
                                                                      "/" = "_or_",
                                                                      "'" = ""))
names(india_cropped) <- str_replace_all(names(india_cropped), c(" " = "_", 
                                                                "/" = "_or_",
                                                                "'" = ""))

# Tidy objects
cameroon_factors <- c("Country", "Language", "Agree", "Littoral", "Gender", "Occupation", "Occupation_Translated", "Occupation_Coded", 
                      "OwnHouse", "CoolHome", "Electricity", "DrinkingWater", "HouseholdPurposes", "RoofingMaterial", "WallMaterial", 
                      "FloorMaterial", "Windows", "NormallyDo", "NormallyDo_Translated", "NormallyDo_Coded", "Add_ventilation", 
                      "Cant_go_outside_(COVID_related)", "Clean_floor_with_water", "Do_nothing_specific", "Draw_curtains", 
                      "Drink_cold_drink", "Drink_warm_drink", "Drink_water", "Go_outside", "Sit_under_tree", "Sleep_outside", 
                      "Sleep_or_lie_on_floor", "Sleep_or_rest", "Smoke", "Stay_at_home", "Take_a_bath_or_shower", "Turn_off_lights", 
                      "Turn_on_power_source", "Unclear", "Undress_or_change_clothes_or_wet_clothes", "Use_a_cooler", "Use_AC", "Use_fan", 
                      "Use_manual_fan_or_cooling_device", "Use_talcum_powder", "PandemicChange", "YesPandemicChange", 
                      "YesPandemicChange_Translated", "YesPandemicChange_Recoded", "SpendAtHome", "AmountOfTime", "Income", 
                      "EatingHabits", "WaterIntake", "ElectricityUsage", "HealthServices", "HomeWarm", "TemperatureInsideHome", 
                      "TemperatureOutsideHome", "Experience_Blurred_vision", "Experience_Clammy_skin", "Experience_Concentration_loss", 
                      "Experience_Confusion", "Experience_Convulsions", "Experience_Dizziness", "Experience_DONT_KNOW", 
                      "Experience_Fainting_[brief_loss_of_consciousness]", "Experience_Fatigue", "Experience_Feeling_hot", 
                      "Experience_Feeling_listless_or_lack_of_engagement_with_social_activities_or_family", "Experience_Feeling_sweaty", 
                      "Experience_Feeling_thirsty", "Experience_Headache", "Experience_Irrational_behaviour", 
                      "Experience_Loss_of_consciousness_[extensive]", "Experience_Muscle_cramps", "Experience_Muscle_weakness", 
                      "Experience_Nausea", "Experience_Poor_quality_of_sleep", "Experience_Rash", "Experience_REFUSED", 
                      "Experience_Vomiting", "ExperienceBeforeHeat", "ExperienceBeforeHeat_(Blurred_vision)", 
                      "ExperienceBeforeHeat_(Clammy_skin)", "ExperienceBeforeHeat_(Concentration_loss)", "ExperienceBeforeHeat_(Confusion)", 
                      "ExperienceBeforeHeat_(Convulsions)", "ExperienceBeforeHeat_(Dizziness)", 
                      "ExperienceBeforeHeat_(Fainting_[brief_loss_of_consciousness])", "ExperienceBeforeHeat_(Fatigue)", 
                      "ExperienceBeforeHeat_(Feeling_hot)", 
                      "ExperienceBeforeHeat_(Feeling_listless_or_lack_of_engagement_with_social_activities_or_family)", 
                      "ExperienceBeforeHeat_(Feeling_sweaty)", "ExperienceBeforeHeat_(Feeling_thirsty)", "ExperienceBeforeHeat_(Headache)", 
                      "ExperienceBeforeHeat_(Irrational_behaviour)", "ExperienceBeforeHeat_(Loss_of_consciousness_[extensive])", 
                      "ExperienceBeforeHeat_(Muscle_cramps)", "ExperienceBeforeHeat_(Muscle_weakness)", "ExperienceBeforeHeat_(Nausea)", 
                      "ExperienceBeforeHeat_(Poor_quality_of_sleep)", "ExperienceBeforeHeat_(Rash)", "ExperienceBeforeHeat_(Vomiting)", 
                      "FeelingMore", "PhysicalConflict", "HeatAffect")

cameroon_tidy <- cameroon_read %>% mutate_at(cameroon_factors, factor) %>% rename(Region = Littoral)

indonesia_factors <- c("Country", "Agree", "ADM-1", "Gender", "Occupation", "Occupation_Translated", "Occupation_Coded", "OwnHouse", 
                       "CoolHome", "Electricity",  "DrinkingWater", "HouseholdPurposes", "RoofingMaterial", "WallMaterial", "FloorMaterial", 
                       "Windows", "NormallyDo", "NormallyDo_Translated", "NormallyDo_Coded", "Add_ventilation", 
                       "Cant_go_outside_(COVID_related)", "Clean_floor_with_water", "Do_nothing_specific", "Draw_curtains", 
                       "Drink_cold_drink", "Drink_warm_drink", "Drink_water", "Go_outside", "Sit_under_tree", "Sleep_outside", 
                       "Sleep_or_lie_on_floor", "Sleep_or_rest", "Smoke", "Stay_at_home", "Take_a_bath_or_shower", "Turn_off_lights", 
                       "Turn_on_power_source", "Unclear", "Undress_or_change_clothes_or_wet_clothes", "Use_a_cooler", "Use_AC", "Use_fan", 
                       "Use_manual_fan_or_cooling_device", "Use_talcum_powder", "PandemicChange", "YesPandemicChange", 
                       "YesPandemicChange_Translated", "YesPandemicChange_CodedGeneral", "YesPandemicChange_CodedSpecific", 
                       "SpendAtHome", "AmountOfTime", "Income", "EatingHabits", "WaterIntake", "ElectricityUsage", "HealthServices", 
                       "HomeWarm", "TemperatureInsideHome", "TemperatureOutsideHome", "Experience_Blurred_vision", "Experience_Clammy_skin", 
                       "Experience_Concentration_loss", "Experience_Confusion", "Experience_Convulsions", "Experience_Dizziness", 
                       "Experience_DONT_KNOW", "Experience_Fainting_[brief_loss_of_consciousness]", "Experience_Fatigue", 
                       "Experience_Feeling_hot", "Experience_Feeling_listless_or_lack_of_engagement_with_social_activities_or_family", 
                       "Experience_Feeling_sweaty", "Experience_Feeling_thirsty", "Experience_Headache", "Experience_Irrational_behaviour", 
                       "Experience_Loss_of_consciousness_[extensive]", "Experience_Muscle_cramps", "Experience_Muscle_weakness", 
                       "Experience_Nausea", "Experience_Poor_quality_of_sleep", "Experience_Rash", "Experience_REFUSED", 
                       "Experience_Vomiting", "ExperienceBeforeHeat", "ExperienceBeforeHeat_(Blurred_vision)", 
                       "ExperienceBeforeHeat_(Clammy_skin)", "ExperienceBeforeHeat_(Concentration_loss)", "ExperienceBeforeHeat_(Confusion)", 
                       "ExperienceBeforeHeat_(Convulsions)", "ExperienceBeforeHeat_(Dizziness)", 
                       "ExperienceBeforeHeat_(Fainting_[brief_loss_of_consciousness])", "ExperienceBeforeHeat_(Fatigue)", 
                       "ExperienceBeforeHeat_(Feeling_hot)", 
                       "ExperienceBeforeHeat_(Feeling_listless_or_lack_of_engagement_with_social_activities_or_family)", 
                       "ExperienceBeforeHeat_(Feeling_sweaty)", "ExperienceBeforeHeat_(Feeling_thirsty)", 
                       "ExperienceBeforeHeat_(Headache)", "ExperienceBeforeHeat_(Irrational_behaviour)", 
                       "ExperienceBeforeHeat_(Loss_of_consciousness_[extensive])", "ExperienceBeforeHeat_(Muscle_cramps)", 
                       "ExperienceBeforeHeat_(Muscle_weakness)", "ExperienceBeforeHeat_(Nausea)", 
                       "ExperienceBeforeHeat_(Poor_quality_of_sleep)", "ExperienceBeforeHeat_(Rash)", 
                       "ExperienceBeforeHeat_(Vomiting)", "FeelingMore", "PhysicalConflict", "HeatAffect")

indonesia_tidy <- indonesia_read %>%  mutate_at(indonesia_factors, factor) %>% 
  select(1:114) %>%
  rename("YesPandemicChange_Recoded" = "YesPandemicChange_CodedSpecific") %>%
  select(-YesPandemicChange_CodedGeneral) %>%
  mutate("Language" = NA, .before = "Agree") %>%
  rename("Region" = "ADM-1")

pakistan_factors <- c("Country", "Agree", "Region", "Gender", "Occupation", "Occupation_Translated", "Occupation_Coded", "OwnHouse", 
                      "CoolHome", "Electricity",  "DrinkingWater", "HouseholdPurposes", "RoofingMaterial", "WallMaterial", "FloorMaterial", 
                      "Windows", "NormallyDo", "NormallyDo_Translated", "NormallyDo_Coded", "Add_ventilation", 
                      "Cant_go_outside_(COVID_related)", "Clean_floor_with_water", "Do_nothing_specific", "Draw_curtains", 
                      "Drink_cold_drink", "Drink_warm_drink", "Drink_water", "Go_outside", "Sit_under_tree", "Sleep_outside", 
                      "Sleep_or_lie_on_floor", "Sleep_or_rest", "Smoke", "Stay_at_home", "Take_a_bath_or_shower", "Turn_off_lights", 
                      "Turn_on_power_source", "Unclear", "Undress_or_change_clothes_or_wet_clothes", "Use_a_cooler", "Use_AC", "Use_fan", 
                      "Use_manual_fan_or_cooling_device", "Use_talcum_powder", "PandemicChange", "YesPandemicChange", 
                      "YesPandemicChange_Translated", "YesPandemicChange_Recoded", 
                      "SpendAtHome", "AmountOfTime", "Income", "EatingHabits", "WaterIntake", "ElectricityUsage", "HealthServices", 
                      "HomeWarm", "TemperatureInsideHome", "TemperatureOutsideHome", "Experience_Blurred_vision", "Experience_Clammy_skin", 
                      "Experience_Concentration_loss", "Experience_Confusion", "Experience_Convulsions", "Experience_Dizziness", 
                      "Experience_DONT_KNOW", "Experience_Fainting_[brief_loss_of_consciousness]", "Experience_Fatigue", 
                      "Experience_Feeling_hot", "Experience_Feeling_listless_or_lack_of_engagement_with_social_activities_or_family", 
                      "Experience_Feeling_sweaty", "Experience_Feeling_thirsty", "Experience_Headache", "Experience_Irrational_behaviour", 
                      "Experience_Loss_of_consciousness_[extensive]", "Experience_Muscle_cramps", "Experience_Muscle_weakness", 
                      "Experience_Nausea", "Experience_Poor_quality_of_sleep", "Experience_Rash", "Experience_REFUSED", 
                      "Experience_Vomiting", "ExperienceBeforeHeat", "ExperienceBeforeHeat_(Blurred_vision)", 
                      "ExperienceBeforeHeat_(Clammy_skin)", "ExperienceBeforeHeat_(Concentration_loss)", "ExperienceBeforeHeat_(Confusion)", 
                      "ExperienceBeforeHeat_(Convulsions)", "ExperienceBeforeHeat_(Dizziness)", 
                      "ExperienceBeforeHeat_(Fainting_[brief_loss_of_consciousness])", "ExperienceBeforeHeat_(Fatigue)", 
                      "ExperienceBeforeHeat_(Feeling_hot)", 
                      "ExperienceBeforeHeat_(Feeling_listless_or_lack_of_engagement_with_social_activities_or_family)", 
                      "ExperienceBeforeHeat_(Feeling_sweaty)", "ExperienceBeforeHeat_(Feeling_thirsty)", 
                      "ExperienceBeforeHeat_(Headache)", "ExperienceBeforeHeat_(Irrational_behaviour)", 
                      "ExperienceBeforeHeat_(Loss_of_consciousness_[extensive])", "ExperienceBeforeHeat_(Muscle_cramps)", 
                      "ExperienceBeforeHeat_(Muscle_weakness)", "ExperienceBeforeHeat_(Nausea)", 
                      "ExperienceBeforeHeat_(Poor_quality_of_sleep)", "ExperienceBeforeHeat_(Rash)", 
                      "ExperienceBeforeHeat_(Vomiting)", "FeelingMore", "PhysicalConflict", "HeatAffect")

pakistan_tidy <- pakistan_cropped %>%
  rename("Region" = "ADM-1") %>%
  mutate(Region = Sindh) %>%
  select(-Sindh, -YesPandemicChange_CodedGeneral, -Language2) %>%
  mutate("Occupation_Translated" = NA, .before = "Occupation_Coded") %>%
  rename("YesPandemicChange_Recoded" = "YesPandemicChange_CodedSpecific") %>%
  rename("ExperienceBeforeHeat_(Concentration_loss)" = "ExperineceBeforeHeat_(Concentration_loss)") %>%
  mutate_at(pakistan_factors, factor)

india_factors <- c("Country", "Agree", "Region", "Gender", "Occupation", "Occupation_Translated", "Occupation_Coded", "OwnHouse", 
                   "CoolHome", "Electricity",  "DrinkingWater", "HouseholdPurposes", "RoofingMaterial", "WallMaterial", "FloorMaterial", 
                   "Windows", "NormallyDo", "NormallyDo_Translated", "NormallyDo_Coded", "Add_ventilation", 
                   "Cant_go_outside_(COVID_related)", "Clean_floor_with_water", "Do_nothing_specific", "Draw_curtains", 
                   "Drink_cold_drink", "Drink_warm_drink", "Drink_water", "Go_outside", "Sit_under_tree", "Sleep_outside", 
                   "Sleep_or_lie_on_floor", "Sleep_or_rest", "Smoke", "Stay_at_home", "Take_a_bath_or_shower", "Turn_off_lights", 
                   "Turn_on_power_source", "Unclear", "Undress_or_change_clothes_or_wet_clothes", "Use_a_cooler", "Use_AC", "Use_fan", 
                   "Use_manual_fan_or_cooling_device", "Use_talcum_powder", "PandemicChange", "YesPandemicChange", 
                   "YesPandemicChange_Translated", "YesPandemicChange_Recoded", 
                   "SpendAtHome", "AmountOfTime", "Income", "EatingHabits", "WaterIntake", "ElectricityUsage", "HealthServices", 
                   "HomeWarm", "TemperatureInsideHome", "TemperatureOutsideHome", "Experience_Blurred_vision", "Experience_Clammy_skin", 
                   "Experience_Concentration_loss", "Experience_Confusion", "Experience_Convulsions", "Experience_Dizziness", 
                   "Experience_DONT_KNOW", "Experience_Fainting_[brief_loss_of_consciousness]", "Experience_Fatigue", 
                   "Experience_Feeling_hot", "Experience_Feeling_listless_or_lack_of_engagement_with_social_activities_or_family", 
                   "Experience_Feeling_sweaty", "Experience_Feeling_thirsty", "Experience_Headache", "Experience_Irrational_behaviour", 
                   "Experience_Loss_of_consciousness_[extensive]", "Experience_Muscle_cramps", "Experience_Muscle_weakness", 
                   "Experience_Nausea", "Experience_Poor_quality_of_sleep", "Experience_Rash", "Experience_REFUSED", 
                   "Experience_Vomiting", "ExperienceBeforeHeat", "ExperienceBeforeHeat_(Blurred_vision)", 
                   "ExperienceBeforeHeat_(Clammy_skin)", "ExperienceBeforeHeat_(Concentration_loss)", "ExperienceBeforeHeat_(Confusion)", 
                   "ExperienceBeforeHeat_(Convulsions)", "ExperienceBeforeHeat_(Dizziness)", 
                   "ExperienceBeforeHeat_(Fainting_[brief_loss_of_consciousness])", "ExperienceBeforeHeat_(Fatigue)", 
                   "ExperienceBeforeHeat_(Feeling_hot)", 
                   "ExperienceBeforeHeat_(Feeling_listless_or_lack_of_engagement_with_social_activities_or_family)", 
                   "ExperienceBeforeHeat_(Feeling_sweaty)", "ExperienceBeforeHeat_(Feeling_thirsty)", 
                   "ExperienceBeforeHeat_(Headache)", "ExperienceBeforeHeat_(Irrational_behaviour)", 
                   "ExperienceBeforeHeat_(Loss_of_consciousness_[extensive])", "ExperienceBeforeHeat_(Muscle_cramps)", 
                   "ExperienceBeforeHeat_(Muscle_weakness)", "ExperienceBeforeHeat_(Nausea)", 
                   "ExperienceBeforeHeat_(Poor_quality_of_sleep)", "ExperienceBeforeHeat_(Rash)", 
                   "ExperienceBeforeHeat_(Vomiting)", "FeelingMore", "PhysicalConflict", "HeatAffect")

india_tidy <- india_cropped %>%
  select(-`ADM-1-IND1`, -`ADM-1-IND2`, -`ADM-1-IND3`, -`ADM-1-IND4`) %>%
  rename("Region" = "ADM-1-IND5") %>%
  mutate("Occupation_Translated" = NA, .before = "Occupation_Coded") %>%
  rename("YesPandemicChange_Recoded" = "YesPandemicChange_CodedSpecific") %>%
  select(-YesPandemicChange_CodedGeneral, -Language2) %>%
  rename("Temperature" = "Temperature_(C)", "Heat_Index" = "Heat_Index_(C)") %>%
  mutate_at(india_factors, factor)




# Join data to make a new dataset including ALL four countries - drop one row from Indonesia (lacks Country variable)
# Subsets to only those households with maximum 24 hours per day of electricity
# Also tidy some variables and create new variables
nexusdata <- bind_rows(cameroon_tidy, indonesia_tidy, pakistan_tidy, india_tidy) %>%
  filter(!is.na(Country)) %>%
  mutate(Language = as_factor(Language)) %>%
  mutate(OptIn_Date = dmy(OptIn_Date)) %>%
  mutate(Region = recode(Region, DOUALA = "Douala", 'Jakarta Special Capital Region' = "Jakarta")) %>%
  mutate(City = as_factor(str_c(Country, " - ", Region))) %>%
  mutate(Country = fct_relevel(Country, c("Pakistan",
                                          "India",
                                          "Indonesia",
                                          "Cameroon"))) %>%
  mutate(Gender = as_factor(str_to_sentence(Gender))) %>%
  mutate(DrinkingWater = as_factor(recode(DrinkingWater, 
                                          "BOTTLES" = "bottled water",
                                          'COMMUNAL TAP/SHARED TAP [ALSO CALLED STAND PIPE/STREET END TAP/YARD TAP]' = "shared tap",
                                          'SUPPLIED IN TANKS' = "supplied in tanks",
                                          "OTHER" = "other"))) %>%
  mutate(HouseholdPurposes = recode(HouseholdPurposes,
                                    'COMMUNAL OR STREET-END TAP' = "shared tap",
                                    "OTHER" = "other",
                                    'SUPPLIED IN TANKS' = "supplied in tanks")) %>%
  filter(HoursDay<25) %>% # Remove unrealistic hours of electricity per day
  filter(People_in_House<16) %>% # Remove outliers (99.9% of the data)
  mutate(SpendAtHome = as_factor(str_to_sentence(SpendAtHome))) %>%
  mutate(SpendAtHome = fct_relevel(SpendAtHome, c("Most of the day - except for short trips out", 
                                                "Half of the day", 
                                                "Some of the day",
                                                "Very little - only for sleeping", 
                                                "Other", 
                                                "Don't know", 
                                                "Refused"))) %>%
  mutate(AmountOfTime = as_factor(str_to_sentence(AmountOfTime))) %>%
  mutate(AmountOfTime = fct_relevel(AmountOfTime, c("Significantly increased", 
                                                  "Slightly increased", 
                                                  "Remained about the same",
                                                  "Slightly decreased", 
                                                  "Significantly decreased", 
                                                  "Don't know", 
                                                  "Refused"))) %>%
  mutate(Income = as_factor(str_to_sentence(Income))) %>%
  mutate(Income = fct_relevel(Income, c("Significantly increased", 
                                                    "Slightly increased", 
                                                    "Remained about the same",
                                                    "Slightly decreased", 
                                                    "Significantly decreased", 
                                                    "Don't know", 
                                                    "Refused"))) %>%
  mutate(Occupation_Coded = as_factor(str_to_sentence(Occupation_Coded))) %>%
  mutate(EatingHabits = fct_relevel(EatingHabits, c("I EAT MORE - IT IS ENOUGH", 
                                        "NO CHANGE - IT IS ENOUGH", 
                                        "NO CHANGE - IT IS NOT ENOUGH", 
                                        "I EAT LESS - IT IS ENOUGH",
                                        "I EAT LESS - IT IS NOT ENOUGH", 
                                        "DON'T KNOW"))) %>%
  mutate(WaterIntake = as_factor(str_to_sentence(WaterIntake))) %>%
  mutate(WaterIntake = as_factor(recode(WaterIntake, 
                                        "Yes - i use more" = "Yes - I use more",
                                        "Yes - i use less" = "Yes - I use less"))) %>%
  mutate(WaterIntake = fct_relevel(WaterIntake, c("Yes - I use more",
                                                  "No - no change",
                                                  "Yes - I use less",
                                                  "Don't know",
                                                  "Refused"))) %>%
  mutate(ElectricityUsage = fct_relevel(ElectricityUsage, c("I HAVE BEEN ABLE TO USE MORE ENERGY",
                                                  "NO CHANGE",
                                                  "I HAVE HAD TO USE LESS ENERGY",
                                                  "DON'T KNOW",
                                                  "REFUSED"))) %>%
  mutate(ElectricityUsage = as_factor(str_to_sentence(ElectricityUsage))) %>%
  mutate(Electricity = str_to_sentence(Electricity)) %>%
  mutate(Electricity = fct_relevel(Electricity, c("Solar", 
                                                  "Generator",
                                                  "12-volt battery",
                                                  "Informal connection",
                                                  "Other"))) %>%
  mutate(HealthServices = as_factor(str_to_sentence(HealthServices))) %>%
  mutate(HealthServices = fct_relevel(HealthServices, c("Harder", 
                                        "The same", 
                                        "Easier", 
                                        "Refused",
                                        "Don't know"))) %>%
  mutate(HomeWarm = as_factor(str_to_sentence(HomeWarm))) %>%
  mutate(HomeWarm = fct_relevel(HomeWarm, c("Very hot",
                                            "Hot",
                                            "Warm",
                                            "Neutral",
                                            "Slightly cool",
                                            "Cool",
                                            "Cold",
                                            "Don't know"))) %>%
  mutate(TemperatureInsideHome = as_factor(str_to_sentence(TemperatureInsideHome))) %>%
  mutate(TemperatureInsideHome = fct_relevel(TemperatureInsideHome, c("Very hot",
                                            "Hot",
                                            "Warm",
                                            "Slightly warm",
                                            "Comfortable",
                                            "Slightly cool",
                                            "Cool",
                                            "Cold",
                                            "Don't know",
                                            "Refused"))) %>%
  mutate(TemperatureOutsideHome = as_factor(str_to_sentence(TemperatureOutsideHome))) %>%
  mutate(TemperatureOutsideHome = fct_relevel(TemperatureOutsideHome, c("Intolerable", 
                                                                        "Very difficult to tolerate", 
                                                                        "Fairly difficult to tolerate",
                                                                        "Slightly difficult to tolerate",
                                                                        "Perfectly tolerable",
                                                                        "Don't know",
                                                                        "Refused"))) %>%
  mutate(Experience_DONT_KNOW = fct_explicit_na(Experience_DONT_KNOW, na_level = "No")) %>%
  select(!c(Agree, Occupation, Occupation_Translated, YesPandemicChange, YesPandemicChange_Translated, NormallyDo, NormallyDo_Translated))


# Make some new variables
newnexus <- nexusdata %>%
  mutate(Employment = as_factor(recode(Occupation_Coded, # Note that this has already been done into four categories - see report
                                       'EMPLOYED IN PRIVATE COMPANY' = "Working",
                                       'GOVERNMENT WORK' = "Working",
                                       'PAID LABOR/WAGE' = "Working",
                                       'HOUSEWIFE' = "Homemaker",
                                       'HOUSEWIFE/HUSBAND/HOMEMAKER' = "Homemaker",
                                       'AGRICULTURE/HUSBANDRY/FISHING' = "Working",
                                       'INFORMAL TRADE / STREET SALES' = "Working",
                                       'BUSINESS/SELF-EMPLOYED' = "Working",
                                       'STUDENT' = "Student",
                                       'UNEMPLOYED' = "Not working",
                                       'GOVERNMENT ASSISTANCE / SOCIAL PROTECTION NETWORKS [PENSIONS]' = "Not working",
                                       'MIGRANT REMITTANCES [FROM OUTSIDE OF THE COUNTRY]' = "Not working",
                                       'GOVERNMENT ASSISTANCE/SOCIAL PROTECTION NETWORKS [PENSIONS]' = "Not working",
                                       'HELP FROM FAMILY AND/OR FRIENDS [FROM INSIDE OF THE COUNTRY]' = "Not working"))) %>%
  mutate(wallmass = recode(WallMaterial,
                           "BAMBOO POLES OR WOOD" = "Lightweight",
                           "MUD BRICK [CRUDE/RAW/COOKED]" = "Heavyweight",
                           "PALM FRONDS/WOVEN MATS" = "Lightweight",
                           "REINFORCED CONCRETE" = "Heavyweight",
                           "DON'T KNOW"= "Don't know",
                           "REFUSED" = "Refused")) %>%
  mutate(roofmass = recode(RoofingMaterial,
                           "BAMBOO" = "Lightweight",
                           "GALVANISED IRON"= "Lightweight",
                           "PALM FRONDS"= "Lightweight",
                           "REINFORCED CONCRETE"= "Heavyweight",
                           "SHEET METAL [TIN/ZINC/CORRUGATED IRON]"= "Lightweight",
                           "T-GIRDER"= "Heavyweight",
                           "WOODEN SHINGLES"= "Lightweight",
                           "CERAMIC TILE"= "Heavyweight",
                           "DON'T KNOW"= "Don't know",
                           "DRIED CLAY"= "Heavyweight",
                           "REFUSED" = "Refused")) %>%
  mutate(walltherm = recode(WallMaterial, "BAMBOO POLES OR WOOD" = "Lightweight low thermal conductivity",
                            "MUD BRICK [CRUDE/RAW/COOKED]" = "Heavyweight low thermal conductivity",
                            "PALM FRONDS/WOVEN MATS" = "Lightweight low thermal conductivity",
                            "REINFORCED CONCRETE" = "Heavyweight high thermal conductivity",
                            "DON'T KNOW"= "Don't know",
                            "REFUSED" = "Refused")) %>%
  mutate(rooftherm = recode(RoofingMaterial,
                            "BAMBOO" = "Lightweight low thermal conductivity",
                            "GALVANISED IRON"= "Lightweight high thermal conductivity",
                            "PALM FRONDS"= "Lightweight low thermal conductivity",
                            "REINFORCED CONCRETE"= "Heavyweight high thermal conductivity",
                            "SHEET METAL [TIN/ZINC/CORRUGATED IRON]"= "Lightweight high thermal conductivity",
                            "T-GIRDER"= "Heavyweight high thermal conductivity",
                            "WOODEN SHINGLES"= "Lightweight low thermal conductivity",
                            "CERAMIC TILE"= "Heavyweight low thermal conductivity",
                            "DON'T KNOW"= "Don't know",
                            "DRIED CLAY"= "Heavyweight low thermal conductivity",
                            "REFUSED" = "Refused")) %>%
  mutate(TemperatureInsideHomeCat = recode(TemperatureInsideHome,
                                          "very hot" = "Hot", 
                                          "hot" = "Hot", 
                                          "warm" = "Hot", 
                                          "slightly warm" = "Hot", 
                                          "comfortable" = "Comfortable",
                                          "slightly cool" = "Cold",
                                          "cool" = "Cold",
                                          "cold" =  "Cold")) %>%
  mutate(HoursDayCat = cut(HoursDay, breaks=seq(0,24,4), labels=c("0-4","5-8","9-12", "13-16", "17-20", "21-24"))) %>%
  mutate(pseudoID = row_number()) %>%
  mutate(WallMaterial = str_to_sentence(WallMaterial)) %>%
  mutate(RoofingMaterial = str_to_sentence(RoofingMaterial))

nexusNormallyDo <- newnexus %>% pivot_longer(cols = Add_ventilation:Use_talcum_powder,
                                             names_to = "NormallyDo_New",
                                             values_to = "NormallyDo_Selection") %>%
  group_by(NormallyDo_New, NormallyDo_Selection) %>%
  mutate(NormallyDo_New = as_factor(NormallyDo_New))

nexusExperience <- newnexus %>% pivot_longer(cols = Experience_Blurred_vision:Experience_Vomiting,
                                             names_to = "Experience_New",
                                             values_to = "Experience_Selection") %>%
  group_by(Experience_New, Experience_Selection) %>%
  mutate(Experience_New = as_factor(Experience_New))

nexusExperienceBeforeHeat <- newnexus %>% pivot_longer(cols = `ExperienceBeforeHeat_(Blurred_vision)`:`ExperienceBeforeHeat_(Vomiting)`,
                                                       names_to = "ExperienceBeforeHeat_New",
                                                       values_to = "ExperienceBeforeHeat_Selection") %>%
  group_by(ExperienceBeforeHeat_New, ExperienceBeforeHeat_Selection) %>%
  mutate(ExperienceBeforeHeat_New = as_factor(ExperienceBeforeHeat_New))



```

# Graphs from previous paper

## Pakistan 

```{r, cache = TRUE}

library(tidyverse)

# Graphs for report

cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") # From http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/#a-colorblind-friendly-palette
newnames <- names(newnexus)


# Page 33
ggplot(data = newnexus %>% filter(Country=="Pakistan") %>%
         pivot_longer(cols = Add_ventilation:Use_talcum_powder,
                      names_to = "NormallyDo_New",
                      values_to = "Selection") %>%
         group_by(NormallyDo_New, Selection) %>%
         mutate(NormallyDo_New = as_factor(NormallyDo_New)) %>%
         summarise(n = n()) %>%
         filter(Selection == "Yes") %>%
         arrange(desc(n))) +
  geom_bar(aes(x = reorder(NormallyDo_New, n), y = n), stat = "identity", fill = "#999999") +
  scale_x_discrete(labels = c("Take_a_bath_or_shower" = "Take a bath or shower",
                              "Go_outside" = "Go outside",
                              "Use_fan" = "Use fan",
                              "Do_nothing_specific" = "Do nothing specific",
                              "Drink_cold_drink" = "Drink cold drink",
                              "Use_manual_fan_or_cooling_device" = "Use manual fan or cooling device",
                              "Add_ventilation" = "Add ventilation",
                              "Turn_on_power_source" = "Turn on power source",
                              "Drink_water" = "Drink water",
                              "Use_a_cooler" = "Use a cooler",
                              "Clean_floor_with_water" = "Clean floor with water",
                              "Stay_at_home" = "Stay at home",
                              "Sleep_or_rest" = "Sleep or rest",
                              "Use_talcum_powder" = "Use talcum powder",
                              "Use_AC" = "Use AC",
                              "Undress_or_change_clothes_or_wet_clothes" = "Undress or change clothes or wet clothes",
                              "Drink_warm_drink" = "Drink warm drink",
                              "Sit_under_tree" = "Sit under tree")) +
  xlab("Strategies normally used to manage heat") +
  coord_flip()

# Page 34
newnexus %>% 
  filter(Country == "Pakistan") %>%
  group_by(SpendAtHome, Gender) %>%
  count() %>%
  group_by(Gender) %>%
  mutate(percentage = n/sum(n)) %>%
  ggplot(aes(x = SpendAtHome, y = percentage, fill = Gender)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = cbPalette) +
  xlab("Time spent at home before pandemic") +
  ylab("Percentage of group")

# Page 35
newnexus %>% 
  filter(Country == "Pakistan") %>%
  group_by(AmountOfTime, Gender) %>%
  count() %>%
  group_by(Gender) %>%
  mutate(percentage = n/sum(n)) %>%
  ggplot(aes(x = AmountOfTime, y = percentage, fill = Gender)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = cbPalette) +
  xlab("Change in time at home after pandemic") +
  ylab("Percentage of group")

# Page 36
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
  geom_bar(aes(x = Electricity), position = "dodge", fill = "#999999") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  ylab("n") +
  xlab("Source of electricity")

ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
  geom_bar(aes(x = HoursDay), fill = "#999999") +
  scale_x_continuous(breaks=seq(0,24,1)) +
  xlab("Hours of electricity per day") +
  ylab("n")

# Page 37
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
  geom_bar(aes(x = Electricity, fill = HoursDayCat), position = "dodge") +
  scale_fill_manual(values = cbPalette) +
  xlab("Source of electricity") +
  ylab("n") +
  labs(fill = "Hours of electricity per day")
  
# Page 38
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
  geom_bar(aes(x = reorder(ElectricityUsage, desc(ElectricityUsage))), fill = "#999999") +
  coord_flip() +
  xlab("Change in use of electricity") +
  ylab("n") +
  theme(legend.position = "none") 

ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
  geom_bar(aes(x = reorder(ElectricityUsage, desc(ElectricityUsage)), fill = City), position = "dodge") +
  coord_flip() +
  scale_fill_manual(values = cbPalette) +
  xlab("Change in use of electricity") +
  ylab("n") +
  facet_grid(rows = vars(City)) +
  theme(legend.position = "none")

# Page 39
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
  geom_bar(aes(x = reorder(WaterIntake, desc(WaterIntake))), fill = "#999999") +
  coord_flip() +
  xlab("Change in water intake") +
  ylab("n")
  
ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
  geom_bar(aes(x = reorder(WaterIntake, desc(WaterIntake)), fill = City), position = "dodge") +
  coord_flip() +
  scale_fill_manual(values = cbPalette) +
  xlab("Change in water intake") +
  ylab("n") +
  facet_grid(rows = vars(City)) 
  theme(legend.position = "none")

ggplot(data = newnexus %>% filter(Country=="Pakistan")) +
  geom_bar(aes(x = NormallyDo_Coded)) +
  coord_flip() # clearly some problems with NormallyDo_Coded - too many categories - need to be reorganised according to cats in report page 33

ggplot(data = newnexus %>% filter(Country=="Pakistan")) +
  geom_bar(aes(x = SpendAtHome, fill = Gender), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) # Works okay but needs the categories to be reordered


ggplot(data = newnexus %>% filter(Country=="Pakistan")) +
  geom_bar(aes(x = AmountOfTime, fill = Gender), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) # Works okay but we should use less stereotyping colours for the genders

ggplot(data = newnexus %>% filter(Country=="Pakistan", HoursDay < 25)) +
  geom_histogram(aes(x = HoursDay), position = "dodge") + #Still got some 88 value in here that should go
  scale_x_continuous(n.breaks = 24) +
  stat_ecdf(aes(x = HoursDay), geom = "step", pad = "FALSE")
  geom_line(aes(y = aes(cumsum(HoursDay), x = seq(1:24)))) # This doesn't work

```

## India

```{r, cache = TRUE}
# Page 51
ggplot(data = newnexus %>% filter(Country == "India")) +
  geom_bar(aes(x = Electricity, fill = HoursDayCat), position = "dodge") +
  scale_fill_manual(values = cbPalette) +
  xlab("Source of electricity") +
  ylab("n") +
  labs(fill = "Hours of electricity per day")

ggplot(data = newnexus %>% filter(Country=="India") %>%
         pivot_longer(cols = Add_ventilation:Use_talcum_powder,
                      names_to = "NormallyDo_New",
                      values_to = "Selection") %>%
         group_by(NormallyDo_New, Selection) %>%
         mutate(NormallyDo_New = as_factor(NormallyDo_New)) %>%
         summarise(n = n()) %>%
         filter(Selection == "Yes") %>%
         arrange(desc(n))) +
  geom_bar(aes(x = reorder(NormallyDo_New, n), y = n), stat = "identity", fill = "#E69F00") +
  scale_x_discrete(labels = c("Take_a_bath_or_shower" = "Take a bath or shower",
                              "Go_outside" = "Go outside",
                              "Use_fan" = "Use fan",
                              "Do_nothing_specific" = "Do nothing specific",
                              "Drink_cold_drink" = "Drink cold drink",
                              "Use_manual_fan_or_cooling_device" = "Use manual fan or cooling device",
                              "Add_ventilation" = "Add ventilation",
                              "Turn_on_power_source" = "Turn on power source",
                              "Drink_water" = "Drink water",
                              "Use_a_cooler" = "Use a cooler",
                              "Clean_floor_with_water" = "Clean floor with water",
                              "Stay_at_home" = "Stay at home",
                              "Sleep_or_rest" = "Sleep or rest",
                              "Use_talcum_powder" = "Use talcum powder",
                              "Use_AC" = "Use AC",
                              "Undress_or_change_clothes_or_wet_clothes" = "Undress or change clothes or wet clothes",
                              "Drink_warm_drink" = "Drink warm drink",
                              "Sit_under_tree" = "Sit under tree",
                              "Cant_go_outside_(COVID_related)" = "Can't go outside (COVID-related)")) +
  xlab("Strategies normally used to manage heat") +
  coord_flip()

# Page 52
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
  filter(Country == "India") %>%
  group_by(SpendAtHome, Gender) %>%
  count() %>%
  group_by(Gender) %>%
  mutate(percentage = n/sum(n)) %>%
  ggplot(aes(x = SpendAtHome, y = percentage, fill = Gender)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = cbPalette) +
  xlab("Change in time at home after pandemic") +
  ylab("Percentage of group")


# Page 53
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
  filter(Country == "India") %>%
  group_by(AmountOfTime, Gender) %>%
  count() %>%
  group_by(Gender) %>%
  mutate(percentage = n/sum(n)) %>%
  ggplot(aes(x = AmountOfTime, y = percentage, fill = Gender)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = cbPalette) +
  xlab("Change in time at home after pandemic") +
  ylab("Percentage of group")

```

## Indonesia 

```{r, cache = TRUE}

# Page 66
ggplot(data = newnexus %>% filter(Country == "Indonesia")) +
  geom_bar(aes(x = Occupation_Coded), fill = "#56B4E9") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  xlab("Occupation") +
  ylab("n")

ggplot(data = newnexus %>% filter(Country == "Indonesia")) +
  geom_bar(aes(x = Electricity, fill = HoursDayCat), position = "dodge") +
  ylab("n") +
  xlab("Source of electricity") +
  labs(fill = "Hours of electricity per day")


# Page 67
ggplot(data = newnexus %>% filter(Country=="Indonesia") %>%
         pivot_longer(cols = Add_ventilation:Use_talcum_powder,
                      names_to = "NormallyDo_New",
                      values_to = "Selection") %>%
         group_by(NormallyDo_New, Selection) %>%
         mutate(NormallyDo_New = as_factor(NormallyDo_New)) %>%
         summarise(n = n()) %>%
         filter(Selection == "Yes") %>%
         arrange(desc(n))) +
  geom_bar(aes(x = reorder(NormallyDo_New, n), y = n), stat = "identity", fill = "#56B4E9") +
  scale_x_discrete(labels = c("Use_fan" = "Use fan",
                              "Add_ventilation" = "Add ventilation",
                              "Go_outside" = "Go outside",
                              "Take_a_bath_or_shower" = "Take a bath or shower",
                              "Undress_or_change_clothes_or_wet_clothes" = "Undress or change clothes or wet clothes",
                              "Use_AC" = "Use AC",
                              "Use_manual_fan_or_cooling_device" = "Use manual fan or cooling device",
                              "Drink_water" = "Drink water",
                              "Sleep_or_lie_on_floor" = "Sleep or lie on floor",
                              "Sit_under_tree" = "Sit under tree",
                              "Turn_off_lights" = "Turn off lights",
                              "Sleep_or_rest" = "Sleep or rest")) +
  xlab("Strategies normally used to manage heat") +
  coord_flip()

# Page 68
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
  filter(Country == "Indonesia") %>%
  group_by(SpendAtHome, Gender) %>%
  count() %>%
  group_by(Gender) %>%
  mutate(percentage = n/sum(n)) %>%
  ggplot(aes(x = SpendAtHome, y = percentage, fill = Gender)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = cbPalette) +
  xlab("Time spent at home before pandemic") +
  ylab("Percentage of group")

newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
  filter(Country == "Indonesia") %>%
  group_by(AmountOfTime, Gender) %>%
  count() %>%
  group_by(Gender) %>%
  mutate(percentage = n/sum(n)) %>%
  ggplot(aes(x = AmountOfTime, y = percentage, fill = Gender)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = cbPalette) +
  xlab("Change in time spent at home after pandemic") +
  ylab("Percentage of group")

# Page 74
ggplot(data = newnexus %>% filter(Country=="Indonesia") %>%
         group_by(YesPandemicChange_Recoded) %>%
         summarise(n = n()) %>%
         filter(YesPandemicChange_Recoded != "N/A") %>%
         arrange(-n)) +
  geom_bar(aes(x = reorder(YesPandemicChange_Recoded, n), y = n), stat = "identity", fill = "#56B4E9") +
  xlab("Strategies to manage heat during pandemic") +
  coord_flip()


```

## Cameroon

```{r, cache = TRUE}
# Page 82
ggplot(data = newnexus %>% filter(Country == "Cameroon")) +
  geom_bar(aes(x = Occupation_Coded), fill = "#009E73") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  xlab("Occupation") +
  ylab("n")
  
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
  filter(Country == "Cameroon") %>%
  group_by(Occupation_Coded, Gender) %>%
  count() %>%
  group_by(Gender) %>%
  mutate(percentage = n/sum(n)) %>%
  ggplot(aes(x = Occupation_Coded, y = percentage, fill = Gender)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = cbPalette) +
  xlab("Occupation") +
  ylab("Percentage of group")

# Page 84
ggplot(data = newnexus %>% filter(Country=="Cameroon") %>%
         pivot_longer(cols = Add_ventilation:Use_talcum_powder,
                      names_to = "NormallyDo_New",
                      values_to = "Selection") %>%
         group_by(NormallyDo_New, Selection) %>%
         mutate(NormallyDo_New = as_factor(NormallyDo_New)) %>%
         summarise(n = n()) %>%
         filter(Selection == "Yes") %>%
         arrange(desc(n))) +
  geom_bar(aes(x = reorder(NormallyDo_New, n), y = n), stat = "identity", fill = "#009E73") +
  scale_x_discrete(labels = c("Take_a_bath_or_shower" = "Take a bath or shower",
                              "Go_outside" = "Go outside",
                              "Use_fan" = "Use fan",
                              "Do_nothing_specific" = "Do nothing specific",
                              "Drink_cold_drink" = "Drink cold drink",
                              "Use_manual_fan_or_cooling_device" = "Use manual fan or cooling device",
                              "Add_ventilation" = "Add ventilation",
                              "Turn_on_power_source" = "Turn on power source",
                              "Drink_water" = "Drink water",
                              "Use_a_cooler" = "Use a cooler",
                              "Clean_floor_with_water" = "Clean floor with water",
                              "Stay_at_home" = "Stay at home",
                              "Sleep_or_rest" = "Sleep or rest",
                              "Use_talcum_powder" = "Use talcum powder",
                              "Use_AC" = "Use AC",
                              "Undress_or_change_clothes_or_wet_clothes" = "Undress or change clothes or wet clothes",
                              "Drink_warm_drink" = "Drink warm drink",
                              "Sit_under_tree" = "Sit under tree",
                              "Cant_go_outside_(COVID_related)" = "Can't go outside (COVID-related)",
                              "Draw_curtains" = "Draw curtains",
                              "Sleep_or_lie_on_floor" = "Sleep or lie on floor")) +
  xlab("Strategies normally used to manage heat") +
  coord_flip()

# Page 85
ggplot(data = newnexus %>% filter(Country == "Cameroon")) +
  geom_bar(aes(x = Electricity, fill = HoursDayCat), position = "dodge") +
  ylab("n") +
  xlab("Source of electricity") +
  labs(fill = "Hours of electricity per day")

# Page 86
newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
  filter(Country == "Cameroon") %>%
  group_by(SpendAtHome, Gender) %>%
  count() %>%
  group_by(Gender) %>%
  mutate(percentage = n/sum(n)) %>%
  ggplot(aes(x = SpendAtHome, y = percentage, fill = Gender)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = cbPalette) +
  xlab("Time spent at home before pandemic") +
  ylab("Percentage of group")

newnexus %>% # Fantastic example from Simon Brauer https://stackoverflow.com/questions/37008705/ggplot-bar-chart-of-percentages-over-groups
  filter(Country == "Cameroon") %>%
  group_by(AmountOfTime, Gender) %>%
  count() %>%
  group_by(Gender) %>%
  mutate(percentage = n/sum(n)) %>%
  ggplot(aes(x = AmountOfTime, y = percentage, fill = Gender)) +
  geom_bar(position = "dodge", stat = "identity") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_y_continuous(labels = scales::percent) +
  scale_fill_manual(values = cbPalette) +
  xlab("Change in time spent at home after pandemic") +
  ylab("Percentage of group")
```

## All countries

```{r, cache = TRUE}
# Page 97
      # No age distribution data in my data sets

ggplot(data = newnexus) +
  geom_bar(aes(x = Country, fill = Gender), position = "dodge") +
  scale_fill_manual(values = cbPalette) +
  ylab("n")

# Page 98
ggplot(data = newnexus) +
  geom_bar(aes(x = Occupation_Coded, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  xlab("Occupation") +
  ylab("n")

# Page 99
ggplot(data = newnexus) +
  geom_bar(aes(x = RoofingMaterial, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1)

# Page 100
ggplot(data = newnexus) +
  geom_bar(aes(x = WallMaterial, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1)

ggplot(data = newnexus) +
  geom_bar(aes(x = FloorMaterial, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1)

# Page 103
ggplot(data = newnexus) +
  geom_bar(aes(x = TemperatureOutsideHome, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1) +
  xlab("Thermal comfort outside the home")

ggplot(data = newnexus) +
  geom_bar(aes(x = TemperatureInsideHome, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1) +
  xlab("Thermal comfort inside the home")

# Page 105
ggplot(data = newnexus) +
  geom_bar(aes(x = SpendAtHome, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1) +
  xlab("Time spent at home before pandemic") +
  ylab("n")

ggplot(data = newnexus) +
  geom_bar(aes(x = AmountOfTime, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1) +
  xlab("Change in time spent at home after pandemic") +
  ylab("Percentage of group")

# Page 108
ggplot(data = newnexus) +
  geom_bar(aes(x = HoursDay, fill = Country)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1) +
  scale_x_continuous(breaks=seq(0,24,1)) +
  xlab("Hours of electricity per day") +
  ylab("n")


ggplot(data = newnexus) +
  geom_bar(aes(x = TemperatureInsideHome, fill = TemperatureInsideHome)) +
  facet_grid(rows = vars(Country))

ggplot(data = newnexus) +
  geom_bar(aes(x = TemperatureInsideHomeCat, fill = TemperatureInsideHomeCat)) +
  facet_grid(rows = vars(Country))

ggplot(data = newnexus %>% filter(Country == "Pakistan")) +
  geom_bar(aes(x = WaterIntake)) +
  scale_y_continuous(labels = scales::percent)


ggplot(data = nexusdata) +
  geom_point(aes(x = Temperature, y = Relative_Humidity, colour = City, size = Heat_Index)) +
  scale_size_binned(n.breaks = 8)

ggplot(data = nexusdata) +
  geom_point(aes(x = Temperature, y = Relative_Humidity, colour = Country, size = Heat_Index)) +
  scale_size_binned(n.breaks = 8)


ggplot(data = newnexus) +
  geom_jitter(aes(x = Temperature, y = HoursDay, colour = City))


ggplot(data = newnexus) +
  geom_bar(aes(x = Rooms, fill = City), position = "dodge")


ggplot(data = newnexus) +
  geom_bar(aes(x = People_in_House, fill = City), position = "dodge")


ggplot(data = newnexus) +
  geom_bar(aes(x = People_in_House, fill = factor(Rooms)), position = "dodge") +
  scale_x_continuous(n.breaks = 20)


ggplot(data = newnexus) +
  geom_bar(aes(x = People_in_House, fill = factor(Rooms)), position = "dodge") +
  facet_grid(rows = vars(Country)) +
  scale_x_continuous(n.breaks = 20)


ggplot(data = newnexus) +
  geom_bar(aes(x = TemperatureInsideHome, fill = TemperatureOutsideHome)) +
  facet_grid(rows = vars(TemperatureOutsideHome))

ggplot(data = newnexus) +
  geom_bar(aes(x = Temperature, fill = TemperatureOutsideHome)) +
  facet_grid(rows = vars(TemperatureOutsideHome))

ggplot(data = newnexus) +
  geom_bar(aes(x = Temperature, fill = TemperatureInsideHome)) +
  facet_grid(rows = vars(TemperatureInsideHome))


ggplot(data = nexusdata) +
  geom_bar(aes(x = SpendAtHome)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = SpendAtHome, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = SpendAtHome, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))


ggplot(data = nexusdata) +
  geom_bar(aes(x = AmountOfTime)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = AmountOfTime, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = AmountOfTime, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))


ggplot(data = nexusdata) +
  geom_bar(aes(x = Income)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = Income, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = Income, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))


ggplot(data = nexusdata) +
  geom_bar(aes(x = EatingHabits)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = EatingHabits, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = EatingHabits, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))


ggplot(data = nexusdata) +
  geom_bar(aes(x = WaterIntake)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = WaterIntake, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = WaterIntake, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))


ggplot(data = nexusdata) +
  geom_bar(aes(x = ElectricityUsage)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = ElectricityUsage, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = ElectricityUsage, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))



ggplot(data = nexusdata) +
  geom_bar(aes(x = HealthServices)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = HealthServices, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = HealthServices, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))


ggplot(data = nexusdata) +
  geom_bar(aes(x = HomeWarm)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = HomeWarm, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = HomeWarm, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = HomeWarm)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  facet_grid(rows = vars(Country))


ggplot(data = nexusdata) +
  geom_bar(aes(x = TemperatureInsideHome)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = TemperatureInsideHome, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = TemperatureInsideHome, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))


ggplot(data = nexusdata) +
  geom_bar(aes(x = TemperatureOutsideHome)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = TemperatureOutsideHome, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = nexusdata) +
  geom_bar(aes(x = TemperatureOutsideHome, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))



ggplot(data = newnexus) +
  geom_bar(aes(x = WallMaterial)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = WallMaterial)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  facet_grid(rows = vars(Country))

ggplot(data = newnexus) +
  geom_bar(aes(x = wallmass)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  facet_grid(rows = vars(Country))

ggplot(data = newnexus) +
  geom_bar(aes(x = walltherm)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  facet_grid(rows = vars(Country))


ggplot(data = newnexus) +
  geom_bar(aes(x = RoofingMaterial)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = RoofingMaterial)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  facet_grid(rows = vars(Country))

ggplot(data = newnexus) +
  geom_bar(aes(x = roofmass)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  facet_grid(rows = vars(Country))

ggplot(data = newnexus) +
  geom_bar(aes(x = rooftherm)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  facet_grid(rows = vars(Country))




ggplot(data = newnexus) +
  geom_bar(aes(x = wallmass, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = wallmass, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))



ggplot(data = newnexus) +
  geom_bar(aes(x = roofmass)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = roofmass, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = roofmass, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))



ggplot(data = newnexus) +
  geom_bar(aes(x = walltherm)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = walltherm, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = walltherm, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))


ggplot(data = newnexus) +
  geom_bar(aes(x = rooftherm)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = rooftherm, fill = City), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = rooftherm, fill = Country), position = "dodge") +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))



ggplot(data = newnexus) +
  geom_bar(aes(x = Temperature, fill = rooftherm)) +
  facet_grid(rows = vars(Country))


ggplot(data = newnexus) +
  geom_bar(aes(x = Windows, fill = rooftherm)) +
  facet_grid(rows = vars(Country))

ggplot(data = newnexus) +
  geom_bar(aes(x = Windows, fill = walltherm)) +
  facet_grid(rows = vars(Country))


ggplot(data = newnexus %>% filter(TemperatureInsideHome!="don't know"&TemperatureInsideHome!="refused")) +
  geom_bar(aes(x = Windows, fill = TemperatureInsideHome)) +
  facet_grid(rows = vars(Country))

ggplot(data = newnexus %>% filter(TemperatureInsideHome!="don't know"&TemperatureInsideHome!="refused")) +
  geom_bar(aes(x = Rooms, fill = TemperatureInsideHome)) +
  facet_grid(rows = vars(Country))


ggplot(data = newnexus %>% filter(TemperatureInsideHome!="don't know"&TemperatureInsideHome!="refused")) +
  geom_bar(aes(x = Windows, fill = Country), position = "dodge") +
  facet_grid(rows = vars(TemperatureInsideHome))


ggplot(data = newnexus) +
  geom_bar(aes(x = wallmass, fill = roofmass))

ggplot(data = newnexus) +
  geom_bar(aes(x = wallmass, fill = roofmass)) +
  facet_grid(rows = vars(Country))

ggplot(data = newnexus) +
  geom_bar(aes(x = walltherm, fill = rooftherm)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

ggplot(data = newnexus) +
  geom_bar(aes(x = walltherm, fill = rooftherm)) +
  facet_grid(rows = vars(Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10))

##########################

ggplot(data = nexusdata) +
  geom_violin(aes(x = City, y = HoursDay))

ggplot(data = nexusdata) +
  geom_histogram(aes(x = HoursDay, fill = City), binwidth = 2) +
    facet_grid(rows = vars(City))

ggplot(data = nexusdata) +
  geom_histogram(aes(x = People_in_House, fill = City), binwidth = 2) +
  facet_grid(rows = vars(City))

ggplot(data = nexusdata) +
  geom_bar(aes(x = Windows, fill = City)) +
  facet_grid(rows = vars(City))


```



# Table 1 creation 

## Characteristics of the population

```{r, cache = TRUE}
library(table1)
nexusdata2<-nexusdata
fac_cols <- sapply(nexusdata, is.character)     
nexusdata2[fac_cols] <- lapply(nexusdata2[fac_cols], as.factor)

#Use the table1 command to produce a table 1
t1<-table1(~ Gender + Occupation_Coded + Language | Country, nexusdata2)
t1

```



## Housing materials and windows

```{r, cache = TRUE}

t2<-table1(~ wallmass + roofmass + walltherm + rooftherm + Windows | Country, data = newnexus)
t2
```



# Regression analysis 

## WallTherm {.tabset}

### Pakistan

Standard model

```{r, message= FALSE}
library(tidyverse)
library(jtools)
library(kableExtra)
library(tidyverse)

newnexus<-filter(newnexus, walltherm != "Don't know") %>% 
  filter(walltherm != "Refused") %>%
  filter(rooftherm != "Refused") %>% 
  filter(rooftherm != "Don't know") %>% 
  mutate(TemperatureInsideHomeCat = recode(TemperatureInsideHome,

                                          "Very hot" = "Hot",

                                          "Hot" = "Hot",

                                          "Warm" = "Hot",

                                          "Slightly warm" = "Hot",

                                          "Comfortable" = "Cold",

                                          "Slightly cool" = "Cold",

                                          "Cool" = "Cold",

                                          "Cold" =  "Cold")) 

newnexus$TemperatureInsideHome <- relevel(newnexus$TemperatureInsideHome, ref = "Cold")

newnexus<-newnexus %>% 
  filter(TemperatureInsideHomeCat != "Don't know") %>% 
  filter(TemperatureInsideHomeCat != "Refused") 

unique(newnexus$TemperatureInsideHomeCat)
summary(newnexus$TemperatureInsideHomeCat)

newnexus <- mutate(newnexus, NormallyDo_Coded = recode(NormallyDo_Coded,
                                                        "Do nothing specific" = "Do nothing",
                                                        "Go outside + Sit under tree" = "Go outside",
                                                        "Unclear" = "Do nothing",
                                                        "Use manual fan/cooling device" = "Use fan",
                                                        "Use cooler" = "Add ventilation",
                                                        "Drink cold drink" = "Drink something",
                                                        "Drink water" = "Drink something",
                                                        "Undress" = "Other",
                                                        "Go outside + Use fan" = "Use fan", 
                                                        "Add ventilation + Go outside" = "Add ventilation",
                                                        "Take bath/shower + Use fan" = "Use fan",
                                                        "Take bath/shower + Go outside" = "Go outside"
  ))

newnexus<-filter(newnexus, NormallyDo_Coded == "Do nothing" | NormallyDo_Coded == "Go outside" | NormallyDo_Coded == "Use fan" | NormallyDo_Coded =="Add ventilation"| NormallyDo_Coded =="Drink something" | NormallyDo_Coded =="Take bath/shower")

newnexus<-newnexus %>% 
  mutate(floortherm = recode(FloorMaterial,

                                          "ASPHALTED FLOOR" = "Heavyweight high thermal conductivity",

                                          "CEMENT" = "Heavyweight high thermal conductivity",

                                          "CERAMIC TILES" = "Heavyweight low thermal conductivity",

                                          "CLAY COURT" = "Heavyweight low thermal conductivity",

                                          "OTHER" = "Other",

                                          "SAND/DIRT" = "Lightweight low thermal conductivity",

                                          "WOOD/BAMBOO" = "Lightweight low thermal conductivity",

                                          "CLAY COAT" =  "Heavyweight low thermal conductivity",
                                "DON'T KNOW" = "Other"))

test<- newnexus %>% 
  group_by(NormallyDo_Coded) %>% 
  count() %>% 
  arrange(-n)

kable(test)

PakistanModel<-filter(newnexus, Country == "Pakistan")

Model1 <- glm(TemperatureInsideHomeCat ~ walltherm, data = PakistanModel, family = binomial())
summ(Model1)
```

Standard model + cooling practices + temperature outside home + Windows + Electricity + Water

```{r}

Model5 <- glm(TemperatureInsideHomeCat ~ walltherm  + TemperatureOutsideHome + Electricity + DrinkingWater + Gender 
, data = PakistanModel, family = binomial())
summ(Model5)

```

### India

Standard model

```{r}

IndiaModel<-filter(newnexus, Country == "India")

Model2 <- glm(TemperatureInsideHomeCat ~ walltherm, data = IndiaModel, family = binomial())
summ(Model2)

```

Standard model + cooling practices + temperature outside home

```{r}

Model5 <- glm(TemperatureInsideHomeCat ~ walltherm + NormallyDo_Coded + TemperatureOutsideHome + DrinkingWater + Gender + SpendAtHome + Windows , data = IndiaModel, family = binomial())
summ(Model5)

```

### Indonesia

Standard model

```{r}

IndonesiaModel<-filter(newnexus, Country == "Indonesia")

Model3 <- glm(TemperatureInsideHomeCat ~ walltherm, data = IndonesiaModel, family = binomial())
summ(Model3)

```

Standard model + cooling practices + temperature outside home

```{r}

Model5 <- glm(TemperatureInsideHomeCat ~ walltherm + NormallyDo_Coded + TemperatureOutsideHome + Gender + Rooms + Windows , data = IndonesiaModel, family = binomial())
summ(Model5)

```

### Cameroon

Standard Model

```{r}
CameroonModel<-filter(newnexus, Country == "Cameroon")

Model4 <- glm(TemperatureInsideHomeCat ~ walltherm, data = CameroonModel, family = binomial())
summ(Model4)

```

Standard model + cooling practices + temperature outside home + Windows + Electricity + Water + Time at home

```{r}

Model5 <- glm(TemperatureInsideHomeCat ~ walltherm + NormallyDo_Coded + TemperatureOutsideHome + Electricity + DrinkingWater + Gender + SpendAtHome, data = CameroonModel, family = binomial())
summ(Model5)

```



## RoofTherm {.tabset}

### Pakistan

Standard model

```{r}
library(tidyverse)
library(jtools)

newnexus<-filter(newnexus, walltherm != "Don't know") %>% 
  filter(walltherm != "Refused") %>%
  filter(rooftherm != "Refused") %>% 
  filter(rooftherm != "Don't know") %>% 
  mutate(TemperatureInsideHomeCat = recode(TemperatureInsideHome,

                                          "Very hot" = "Hot",

                                          "Hot" = "Hot",

                                          "Warm" = "Hot",

                                          "Slightly warm" = "Hot",

                                          "Comfortable" = "Cold",

                                          "Slightly cool" = "Cold",

                                          "Cool" = "Cold",

                                          "Cold" =  "Cold")) 
newnexus$TemperatureInsideHome <- relevel(newnexus$TemperatureInsideHome, ref = "Cold")

newnexus<-newnexus %>% 
  filter(TemperatureInsideHomeCat != "Don't know") %>% 
  filter(TemperatureInsideHomeCat != "Refused") 

PakistanModel<-filter(newnexus, Country == "Pakistan")

Model1 <- glm(TemperatureInsideHomeCat ~ rooftherm, data = PakistanModel, family = binomial())
summ(Model1)
```

Standard model + 529

```{r}

Model10 <- glm(TemperatureInsideHomeCat ~ rooftherm  + TemperatureOutsideHome + Electricity + DrinkingWater + Gender + NormallyDo_Coded + People_in_House 
, data = PakistanModel, family = binomial())
summ(Model10)

```


### India

standard model

```{r}

IndiaModel<-filter(newnexus, Country == "India")

Model2 <- glm(TemperatureInsideHomeCat ~ rooftherm, data = IndiaModel, family = binomial())
summ(Model2)

```

standard model  + 1041

```{r}

Model11 <- glm(TemperatureInsideHomeCat ~ rooftherm  + TemperatureOutsideHome + DrinkingWater + Gender + NormallyDo_Coded + SpendAtHome
, data = IndiaModel, family = binomial())
summ(Model11)

```


### Indonesia

standard model

```{r}
IndonesiaModel<-filter(newnexus, Country == "Indonesia")

Model3 <- glm(TemperatureInsideHomeCat ~ rooftherm, data = IndonesiaModel, family = binomial())
summ(Model3)

```

Standard model + 1102

```{r}

Model12 <- glm(TemperatureInsideHomeCat ~ rooftherm  + TemperatureOutsideHome + Electricity + DrinkingWater + Gender + NormallyDo_Coded + SpendAtHome + Windows + People_in_House 
, data = IndonesiaModel, family = binomial())
summ(Model12)

```


### Cameroon

standard model 

```{r}
CameroonModel<-filter(newnexus, Country == "Cameroon")

Model4 <- glm(TemperatureInsideHomeCat ~ rooftherm, data = CameroonModel, family = binomial())
summ(Model4)

```

standard model + 950

```{r}

Model13 <- glm(TemperatureInsideHomeCat ~ rooftherm  + TemperatureOutsideHome + Gender + NormallyDo_Coded + SpendAtHome + Windows 
, data = CameroonModel, family = binomial())
summ(Model13)

```


## FloorTherm {.tabset}

### Pakistan
```{r}

Model5 <- glm(TemperatureInsideHomeCat ~ floortherm  + TemperatureOutsideHome + Electricity + DrinkingWater + Gender 
, data = PakistanModel, family = binomial())
summ(Model5)


```

### India
Standard model + cooling practices + temperature outside home

```{r}

Model5 <- glm(TemperatureInsideHomeCat ~ floortherm + NormallyDo_Coded + TemperatureOutsideHome + DrinkingWater + Gender + SpendAtHome + Windows , data = IndiaModel, family = binomial())
summ(Model5)

```

### Indonesia

Standard model + cooling practices + temperature outside home

```{r}

Model5 <- glm(TemperatureInsideHomeCat ~ floortherm + NormallyDo_Coded + TemperatureOutsideHome + Gender + Rooms + Windows , data = IndonesiaModel, family = binomial())
summ(Model5)

```

### Cameroon

Standard model + cooling practices + temperature outside home + Windows + Electricity + Water + Time at home

```{r}

Model5 <- glm(TemperatureInsideHomeCat ~ floortherm + NormallyDo_Coded + TemperatureOutsideHome + Electricity + DrinkingWater + Gender + SpendAtHome, data = CameroonModel, family = binomial())
summ(Model5)

```




# Diagram of variable relationships

```{r, out.width = "100%", cache = TRUE}
library(knitr)
include_graphics("Variable_diagram.png")
```



# Testing
```{r}
library(stringr)
library(rstatix)

ggplot(data = newnexus) +
  geom_bar(aes(x = rooftherm, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1) + theme(axis.text.x = element_text(angle = 45, vjust = 0.75))

newnexus %>% 
  group_by(Country, rooftherm) %>% 
  count()

# Page 100
ggplot(data = newnexus) +
  geom_bar(aes(x = walltherm, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1)

newnexus %>% 
  group_by(Country, walltherm) %>% 
  count()

newnexus<- filter(newnexus, floortherm != "Other")

ggplot(data = newnexus) +
  geom_bar(aes(x = floortherm, fill = Country)) +
  scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) +
  scale_fill_manual(values = cbPalette) +
  facet_wrap(facets = vars(Country), ncol = 1) + theme(axis.text.x = element_text(angle = 45, vjust = 0.75))

newnexus %>% 
  group_by(Country, floortherm) %>% 
  count()

cramer_v(newnexus$walltherm, newnexus$rooftherm)

Model5 <- glm(TemperatureInsideHomeCat ~ Temperature, data = newnexus, family = binomial())
summ(Model5)
```

